Validation of Distributed Topology Inference of ...709827/FULLTEXT01.pdf · I want to thanks my friends in KTH ;Shaiyek Taslim Buland , Mainuddin Ahmed Deep,Chaitaniyay Arivind Deshpande

Validation of Distributed Topology

Inference of Distribution Networks using

the IEEE Reliability Test System

KHAN ENAM

Master’s Degree Project

Stockholm, Sweden February 2014

XR-EE-ICS 2014:002

Declaration of Authorship

I, Enam khan, declare that this thesis titled, ‘Online Validation of Distributed Topology

Inference of Electrical Distribution Networks’ and the work presented in it are my own.

I confirm that:

� This work was done wholly or mainly while in candidature for a research degree

at this University.

� Where any part of this thesis has previously been submitted for a degree or any

other qualification at this University or any other institution, this has been clearly

stated.

� Where I have consulted the published work of others, this is always clearly at-

tributed.

� Where I have quoted from the work of others, the source is always given. With

the exception of such quotations, this thesis is entirely my own work.

� I have acknowledged all main sources of help.

� Where the thesis is based on work done by myself jointly with others, I have made

clear exactly what was done by others and what I have contributed myself.

Signed:

Date:

i

“Friendship with everybody enmity with none.”

Enam

Abstract

Reliability of power system depends on the up to date knowledge of the system state for

operation and control. Shifting from large conventional production units to small and/

or renewable DG connected in the distribution network means more control and moni-

toring system require for the Distributed System operator caused by active generation

and reactive power consumption by DG. Therefore it is interesting to explore concepts

in fast and scalable topology processors for monitoring and controlling applications such

as state estimation, OPF and static and dynamic stability assessment in electrical dis-

tribution network the need is evident to validate with meshed network to analyze the

overall performance of the proposed methodology “Decentralized Topology Inference of

Electrical Distribution Networks”. The topology inference processor is require minimal

prior knowledge of electrical network structure by taking a series of time-stamped pro-

cess measurements from each bays of each substation in the network and distinguished

between connected and unconnected bays. This master thesis project has implemented

an IEEE reference electric power distribution network in Simulink platform , integrating

the reference electrical network with the Java-based multi agent topology inference ap-

plication as well as having investigated. This project has included work in the real time

simulation of a standard IEEE reference distribution network, OPC server interfacing

between reference model and the topology inference application, testing and analysis of

the application. The reference model is selected to provide a sufficient case to analyses

and validate the methodology.

Acknowledgements

I would like to express my special appreciation and thanks to my Supervisor MR.

Nicholas Honeth , PHD student at the Department of Industrial Information and Com-

munication System ,The Royal Institute of Technology(KTH) . I have been working

with him more than one year for master thesis , summer internship along with other

two substation automation courses. While working with him I found his vast knowledge

in substation automation and logical thinking in programming language what motivates

me to maintain sheer professionalism in my thesis work. I would like to thank him for

encouraging my research work and allowing me to grow as a research engineer. His

advice on both research as well as on my career have been priceless.

I would also like to thank my, professor Lars Nordstrom for his supervision during

project work of substation automation courses and afterwards in my thesis work as

well by serving as my committee members even at hardship. I also want to thank him

for letting my defense be an enjoyable moment, and for his brilliant comments and

suggestions, thanks to him.

I would like to mention Dr.Arshad Saleem for his fruitful advice and help during my

stay at ICS. I cant help mentioning Mr. Nils Edvinsson lab administrator and Davood

Babazadeh during my entire period stay at ICS.

I want to thanks my friends in KTH ;Shaiyek Taslim Buland , Mainuddin Ahmed

Deep,Chaitaniyay Arivind Deshpande for their support , kindness and useful advice

all the time.

A special thanks to MD.Shahjahan Khan my father and best friend. Words cannot

express how grateful I am to my father for all of the sacrifices that he has made on my

behalf. I also would like to thanks my wife Mary for her continuous support.

iv

Contents

Declaration of Authorship i

Abstract iii

Acknowledgements iv

List of Figures viii

List of Tables x

Abbreviations xi

1 Introduction 1

2 Background 4

2.1 Structure of electric power system . . . . . . . . . . . . . . . . . . . . . . 4

2.2 State estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Network topology Processor . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 Bus/section model . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.2 Bus/branch model . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.3 Distributed Topology Inference processor . . . . . . . . . . . . . . 10

2.4 Decentralized Topology Inference System Architecture . . . . . . . . . . . 10

2.4.1 Multi Agent System . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4.2 IEC 61850 standard for Substation Automation Specification (SAS)communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4.3 Overlay network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.5 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Reference Model Selection 13

3.1 IEEE 30 bus electrical Network . . . . . . . . . . . . . . . . . . . . . . . 13

3.2 IEEE 34 Node test Feeder . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 IEEE 118 Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Medium Voltage (MV) Distribution Network . . . . . . . . . . . . . . . . 15

3.5 Swedish LV Distribution Network . . . . . . . . . . . . . . . . . . . . . . . 15

v

Contents vi

3.6 IEEE RBTS distribution system . . . . . . . . . . . . . . . . . . . . . . 16

3.7 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Algorithm 19

4.1 Managing a friend list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Maintaining the matrix of bays . . . . . . . . . . . . . . . . . . . . . . . . 20

5 Simulation System Architecture 23

5.1 Off-line Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.2 Real Time Interface Architecture . . . . . . . . . . . . . . . . . . . . . . . 24

5.3 Real Time Interface with JAVA API . . . . . . . . . . . . . . . . . . . . . 25

5.4 Real time interface with TCP/IP or UDP/IP communication . . . . . . . 26

5.5 Real time interface with matlabcontrol . . . . . . . . . . . . . . . . . . . 27

5.6 Validation of Offline Model . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.7 Validation of Real time interfacing . . . . . . . . . . . . . . . . . . . . . . 28

6 Implementation 31

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

6.2 Description of the RBTS Bus -4 Distribution Network . . . . . . . . . . . 31

6.3 Load and DG Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

6.4 Power system modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.5 List of SimPowerSystems Blocks Used . . . . . . . . . . . . . . . . . . . . 34

6.5.1 Three phase Programmable Voltage Source . . . . . . . . . . . . . 34

6.5.2 Three phase two winding transformer . . . . . . . . . . . . . . . . 35

6.5.3 Three-Phase V-I Measurement . . . . . . . . . . . . . . . . . . . . 37

6.5.4 Three-Phase Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.5.5 Three-Phase PI Section Line . . . . . . . . . . . . . . . . . . . . . 37

6.5.6 Three-Phase Dynamic Load . . . . . . . . . . . . . . . . . . . . . . 37

6.5.7 Gaussian Noise Generator . . . . . . . . . . . . . . . . . . . . . . . 38

6.5.8 Powergui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.5.9 Discrete 3-phase Positive-Sequence Active and Reactive Power . . 40

6.6 Naming of each bays of the reference network . . . . . . . . . . . . . . . . 41

6.7 RBTS Network build in SimPowerSystem . . . . . . . . . . . . . . . . . . 42

6.8 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.8.1 Machine Initialization . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.8.2 Steady-state voltages and currents . . . . . . . . . . . . . . . . . . 43

6.8.3 Load Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.8.3.1 Machine Initialization . . . . . . . . . . . . . . . . . . . . 45

6.9 Offline Simulation interface . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.10 Real time Simulation interface . . . . . . . . . . . . . . . . . . . . . . . . 46

6.10.1 RT-LAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.10.2 OpComm block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7 Results 48

7.1 Illustrative distribution of incidence certainty . . . . . . . . . . . . . . . . 48

7.2 Incidence certainty distribution for static loads and generators . . . . . . 49

7.3 Incidence certainty distribution for dynamic loads and generators . . . . 49

7.3.1 Incidence certainty distribution with sampling frequency 50 Hz . . 50

Contents vii

7.3.2 Incidence certainty distribution with sampling frequency 25 Hz . . 50

7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

8 Conclusion 54

8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

9 Recommendations for future work 55

9.1 Recommendations for future work . . . . . . . . . . . . . . . . . . . . . . 55

A XML Trees for connected bays 56

B Load flow Table for RBTS 62

C Machine Initialization 67

Bibliography 71

List of Figures

2.1 Structure of electric power system [1]. . . . . . . . . . . . . . . . . . . . . 5

2.2 State Estimation block diagram . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Bus/section model [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Bus/branch model [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.5 Topology inference system architecture [3] . . . . . . . . . . . . . . . . . . 10

3.1 IEEE 30 Bus electrical network . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 IEEE 34 Bus Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 IEEE 118 Bus Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 MV distribution Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16



3.7 IEEE RBTS distribution system of Bus 4 . . . . . . . . . . . . . . . . . . 18

3.8 IEEE RBTS distribution system of Bus 2 . . . . . . . . . . . . . . . . . . 18

4.1 Understanding local context and managing friends list [4]. . . . . . . . . . 20

4.2 Populating and updating the incidnece certinity matrix [4]. . . . . . . . . 22

5.1 Offline Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.2 Real time Interface Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . 25

5.3 Real Time Interface with JAVA API. . . . . . . . . . . . . . . . . . . . . . 26

5.4 Real time interfacing with TCP/IP or UDP/IP communication. . . . . . . 26

5.5 Real time interfacing with matlabcontrole. . . . . . . . . . . . . . . . . . . 27

5.6 RMS current block in simulink. . . . . . . . . . . . . . . . . . . . . . . . . 28

5.7 RMS current block in details. . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.8 OPAL-Rt OPC Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.1 Single line diagram of RBTS [5] . . . . . . . . . . . . . . . . . . . . . . . . 32

6.2 Three phase Programmable Voltage Source . . . . . . . . . . . . . . . . . 35

6.3 Three phase two winding transformer(Parameters) . . . . . . . . . . . . . . . . 36

6.4 Three phase two winding transformer(Configuration) . . . . . . . . . . . . . . 36

6.5 Three phase Programmable Voltage Source(Advanced) . . . . . . . . . . . . . 36

6.6 Three-Phase Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.7 Three-Phase PI Section Line . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.8 Three-Phase Dynamic Load . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.9 Powergui block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.10 Powergui(solver) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.11 Powergui(Load Flow) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6.12 Powergui(Preferences) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

viii

List of Figures ix

6.13 Powergui(Machine initialization tool) . . . . . . . . . . . . . . . . . . . . . 41

6.14 Gaussian Noise Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.15 Discrete 3-phase Positive-Sequence Active and Reactive Power . . . . . . . . . 42

6.16 RBTS Network build with naming tag . . . . . . . . . . . . . . . . . . . . 43

6.17 RBTS Network build in SimPowerSystem . . . . . . . . . . . . . . . . . . 44

6.18 Steady-state voltages and currents . . . . . . . . . . . . . . . . . . . . . . 45

6.19 Load flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.20 RT-LAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.21 OpComm mask when inserting inside a SC subsystem . . . . . . . . . . . . . 47

6.22 OpComm mask when inserting inside a SM or SS subsystem . . . . . . . . . 47

7.1 Illustrative distribution of incidence certainty . . . . . . . . . . . . . . . . 49

7.2 Incidence certainty distribution for static loads and generators at 0.5s . . 50

7.3 Incidence certainty distribution for static loads and generators at 1s . . . . . . . 50

7.4 Incidence certainty distribution for static loads and generators at 1.5s . . . . . 50

7.5 Incidence certainty distribution at a fingerprint window of 1s with sam-pling frequency 50 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.6 Incidence certainty distribution at a fingerprint window of 1.5s with sampling

frequency 50 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.7 Incidence certainty distribution at a fingerprint window of 2s with sampling

frequency 50 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.8 Incidence certainty distribution at a fingerprint window of 1s with sam-pling frequency 25 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.9 Incidence certainty distribution at a fingerprint window of 1.5s with sampling

frequency 25 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.10 Incidence certainty distribution at a fingerprint window of 2s with sampling

frequency 25 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

List of Tables

6.1 System Summary Component[6] . . . . . . . . . . . . . . . . . . . . . . . 32

6.2 System Summary Voltage and Power [6] . . . . . . . . . . . . . . . . . . . 33

6.3 Feeder types and Lengths [6] . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.4 Customer Data [6] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

7.1 Incidence certainty distribution for different sampling frequency and fin-gerprint window size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

x

Abbreviations

AOS Agent Oriented Software

API Application Protocol Interface

OLE Object Linking and Embading

RTU Remot Terminal Unit

DSC Distributed Controle System

SAS Substation Automation System

xi

Dedicated to my father MD.Shahjahan Khan

xii

Chapter 1

Introduction

The effective and reliable operation of the power system depends on the accurate models

of the topology to monitor and control applications i.e. State Estimation (SE), Optimal

Power flow analysis and dynamic stability analysis. A centralized system for monitoring

and control called Supervisory Control and Data Acquisition (SCADA) system is used

to collect two types of measurements. The first measurements are the binary statuses of

circuit breakers and switches. The other measurements are the analogue measurements

which consist of active and reactive power, voltage magnitude from lines [7]. These

measurements are used by state estimator for control and monitor of the power system.

State estimator provides the state of unobservable part of real power system from the real

time topology of the power system. Therefor an accurate model of the network topology

update in real time is the critical part for control, monitoring and operation of a reliable

power system [3]. Existing methods for determining the topology of the power system is

based on the logical signal or the status of the circuit breaker and the switches. “If an

erroneous status should for some reason be reported by the field devices i.e. collecting

measurements from circuit breakers or the misconfiguration of the system or poor data

acquired from the remote terminal unit (RTU), the result of state estimation, Optimal

Power flow analysis and the dynamic stability analysis will significantly be affect .”[7]

Or, if for some reason there are changes in the distribution network due to isolation of

some part or due to maintenance work being carried out, the system will be unobservable

because of existing logical status failing to predict the topology, may cause erroneous

action in the power system network. This introduction chapter will briefly describe the

difficulties in implementing traditional state estimation for the distribution network and

presents an alternative solution to reduce topology processing complexity for the state

estimator .

1

Chapter 1. Introduction 2

Distribution networks have a tremendous number of nodes and elements . It is techni-

cally impractical to continuously collect all available data from the distribution network

in real time to always accurately calculate the topology from a central point. Even if it

is possible then the costs would outweigh the benefits of such a system.

Deregulation of the power market has increased the connection of renewable energy

sources as drivers for change in the way distributions networks are planned, managed

and operated. On the 1st July 2007 all 27 EU countries were opened in the market

to exercise their market power in the energy sector. The main goal of deregulation is

to make the market more competitive and give the incentives to new generation and

exposition of the existing network [8]. Hence large conventional production units are

shifting to small and/ or renewable distributed generation units. Secondly, distributed

generation becomes a commonplace as several countries in the EU offer incentives to

install solar and wind power generation and the certificate system in the green investment

which makes it more attractive to invest in renewable energy. So, more distributed

generation introduced in the distribution network means more dynamic power flows in

to the distribution system. The power systems of the future will most likely be more

complex than they have been in the past. As a result it makes the power system more

dependent on the state estimation for the unobservable state of the distribution network

and hence the model of the network topology update in real time is required of the

distribution network.

Continuous changes in the production or in the consumption side due to makes the re-

liability power system operation more complex , which requires the system to exercise

more control and monitoring operations to handle problems introduced by distributed

generators[9]. All the above mentioned causes require lots of maintenance, documenta-

tion, updates and sharing real time models of the inter-connected power system which

is a challenging task and needs robust and scalable management tools between various

monitoring and control system for the distribution network. State estimation uses the

real time model of the power system to determine the unobservable state of the power

system as a state vector which can be used by many of these control and monitoring

application. But in the existing way of determining topology of distribution network in

real time for the state estimation does not scale well for distribution network applications

because it requires continuously measuring and communicating data from tremendous

number of nodes and elements. If there is error in the topology due to telemetry or there

can be errors in the static model of the network used for topology processing the state

estimation will no longer be valid and the state estimator yield incorrect result.

One proposed solution is the distributed topology inference application to reduce topol-

ogy processing complexity and reduce costs associated with static model management

Chapter 1. Introduction 3

and configuration of networks with very many nodes. The distributed topology of in-

ference application(DTIA) is a distributed application among agents mapped in each

substations of the distribution network. So, there is no central software process in the

electrical distribution network, just distributed among agents mapped to different sub-

stations. Each agent that is executing its part of the algorithm or the application, has

a table or collection of data about its local bays. Bays of substation are the connection

point feeders and lines.Each agent records time stamped samples from its local bays.

When the algorithm is run in the agent of station, it will exchange time stamped sam-

ples of local bays to other agents to perform correlation . If the pairs of time stamped

samples are indeed connected there should be a very strong correlation result whereas

unconnected pairs will have significantly weaker correlation. The distributed topology

inference application is a best-effort service to infer the topology of the electrical distri-

bution network by examining these correlations of pairs of time stamped samples. This

thesis aims to provide a validation of the DTIA methodology proposed in [4] by using

a distributed multi-agent based method for inferring the grid topology from the process

measurement data by using analogue measurements for topology application.

Distributed topology processor application could be implemented by using the mea-

surements of current from the network which require minimal prior knowledge of the

electrical network structure support power operations and decision making of the power

system. The DTIA is a multi agent based application and capable of communicating

on the IEC 61850 station bus to other DTIA agents located in the stations of electri-

cal distribution network. These DTIAs will be interfacing with substation automation

devices to collect current measurements. “The capabilities of structured information

exchange and interfacing of substation automation devices enables plug-and-play oper-

ation of the topology inference requiring minimal prior knowledge of electrical network

structure ”[4].

This project is a continuation of work previously presented in[3] and [4] to validate the

methodology with a IEEE electrical distribution network. The electrical distribution

network has built in Simulink platform and the DTIA application has built in the Java

platform.

The rest of the report is organized as follows. Chapter 2 briefly describes the background.

Chapter 3 presents the reference model selection. Chapter 4 describes the algorithm part

of the application, Chapter 5 describes the simulation system architecture. Chapter 6

briefly describes the implementation of electrical reference network , Chapter 7 Results

and finally in Chapter 8 is the conclusion and recommendation for future work.

Chapter 2

Background

2.1 Structure of electric power system

A power system consists of generation sources, transmission lines and transformers to

transmits generated power and the distribution system to distribute the load.Details

power system structure is given in picture 2.1. The Main power sources are connected

to transmission network. The Swedish Power transmission network consists of approx-

imately 15250 Km power lines in 23 different locations own by Transmission System

Operator (TSO) Sevenska Kraftnat. These transmission lines transmit power from gen-

eration unit to load area.

Sub transmission networks also transmit energy to load area but amount of transmitted

energy and transmission distance are smaller compare to transmission network with

lower voltage level.

The distribution network transmits and distributes electrical energy from subs transmis-

sion to end load. Industrial loads are connected in high voltage level and local consumers

are connected to low voltage level of distribution network.

2.2 State estimation

Power system control and operation depends on accurate network model in real time.

An accurate topology of the power system network gives accurate result in the Energy

management system (EMS) application i.e. state estimation (SE) and Optimal power

flow (OPF) and other applications . State estimation provides the real time system

condition of power system that comes from real time primary data of SCADA system.

The SCADA system collects time stamped measurements(but most older RTUs doesn’t

4

Chapter 2. Background 5

Transmission network400-200 kV

(Svenska Kraftnät)

Sub-transmission network130-40 kV

Distribution networkPrimary part

40-10 kV

Distribution networkSecondary part

Low voltage 230/400 V

Figure 2.1: Structure of electric power system [1].

provide with time stamp ) and status from remote terminal unit (RTUs) in real time,

installed in substations of the network. RTUs provide magnitude of voltages and currents

also active and reactive power. Network Topology processor can receives status of the

circuit breaker from RTU as well as from the SCADA system to determine the network

model.State estimation solution determines most likely state of the system composed

of complex bus voltages in the entire power system as well as best estimates for line

flows, loads, generation outputs. It is based on the system model and the obtained

measurements.

State estimation consists of following steps shown in figure 2.2,

• Data acquisition from SCADA system

• Network topology processing

• Observability analysis

• Estimate the state vector

• Bad data output

Many criteria used to develop state estimator, the following three are regarded as the

most common,

• Maximum Likelihood: maximizes the probability that the estimated state variable

is near the true value.


Figure 2.2: State Estimation block diagram .

• Weighted Least Squares (WLS): minimizes the sum of the squared weighted resid-

uals between the estimated and actual measurements.

• Minimum Variance: minimizes the expected value of the sum of the squared residu-

als between components of the estimated state variable and the true state variable.

The errors are assumed to be stochastic and to have the following properties:

• All measurement errors are assumed as following Gaussian Distribution with known

standard deviation.

• Mathematical expectation of the measurement error is zero E(ei ) = 0, i = 0,1,...,m

• Measurement errors are independent, i.e. E(eiej ) = 0

Weighted matrix construction :

State variables in power system usually are voltage magnitudes and angles in the nodes

of the power system. Other quantities e.g. bus power injection, branch power flows and

current could be determined through some function h(x)by the state vector [10]:

XT = (θ1, · · · , θn, V1 · · · , Vn) (2.1)

Zj = hi(X) + ej (2.2)

Where x is the true state vector, zj is the jth measurement, hj relates the jth measure-

ment to states, ej is the measurement error.


The joint probability density function, which represents the probability of measuring m

independent measurement [10]:

fm(Z) = f(Z1) · f(Z2) · · · f(Zm) (2.3)

where Zi is the ith measurement. The function fm(Z) is called the likelihood function

for Z . The objective of maximum likelihood is to maximize this likelihood function

by varying its mean µ , and its standard deviation σ . In determine the optimum

parameter values, the function are replaced by its logarithm. The modified function is

called Log-Likelihood Function and is given by [10]:

log fm(Z) =

m∑i=1

log f(Zi) = −1

2

m∑i=1

(Zi − µiσi

)2 − m

2· log 2π −

∑s

ummi=1 log σi (2.4)

Maximum Likelihood Estimation (MLE) will maximize the likelihood function for a

given set of observation Z1, Z2 ,· · · , Zm. It can be obtained by solving the following

problem [10]:

min∑

(Zi − hi(x)

σi)2 (2.5)

The minimization problem can be re-written in terms of the residual ri of measurement

I, which is defined as [10]:

ri = Zi − µi = E(Zi) (2.6)

where the mean µi, or the expected value E(Ei) , of the measurement Zi can be ex-

pressed as 4 hi(x) , a nonlinear function relating the system state vector X to the ith

measurement. Square of each residual ri 2 is weighted byWii = σi−2,which is inversely

related to the assumed error variance for that measurement[10].

ri2 = Wii · ri2 (2.7)

Using the Newtons iteration as shown below could solve the estimation problem: WLS

objective function [10]:

J(X) =∑

(Zi − hi(X)

σi)2 (2.8)

The solution for objective function J(x) [10]:

1. At the minimum, the first order optimality conditions will have to be satisfied [10]:

g(X) =δJ(X)

δX= HT (X)R−1 · [Z − h(X)] = 0[10] (2.9)

where, H(X) = [ δh(X)δX ]


2. Expanding the g(x) into its Taylor series around state vector

XK [10]: g(x) = g(Xk) +G(Xk)(X −XK) + ... = 0 where,

G(Xk) =δg(xK)

δX= HT (XK)R−1H(XK) (2.10)

3. Neglecting the higher order terms leads to an iterative solutions scheme know as

the Gauss-Newton method as [10]:

XK+1 = XK − [GXK ]−1.g(XK) where 4X could be solved as,

4XK = −[G(XK)]−1 · g(X−1) (2.11)

4. Test convergence max(|4XK |) ≤ ζIf the calculation is not converged [10]

XK+1 = XK +4XK (2.12)

K = K + 1 go back to step 3.

2.3 Network topology Processor

The Network topology processor determines the connection of the electrical network and

the location of the metering device in the reference network. A database is assumed to

contain all information of metering device along with their bus section and switching

devices (Circuit breakers and switches). Conventional topology processing statuses of

switching devices are collect by telemetered or system operator entered manually. Gen-

erally network topology of reference network is determined before the state estimation

steps. State estimator uses bus/brunch model of the reference network whereas network

connectivity (physical level representation) of switching devices of reference model de-

scribes in bus/section model of the reference network. Network topology processor also

transforms bus/section model of the reference network in to bus/brunch model for state

estimator and other network analysis function[2].

2.3.1 Bus/section model

This model is based on determining the connectivity of a bus section group. When

the entire switching device i.e. breakers and switches are closed of the corresponding

section than all switching devices will merge into a single bus and becomes bus sec-

tion group. Bus/section model is determined by the relevant data structure of Network


topology processor. Network topology processor updates the part of the reference net-

work (bus/section) when there is a status change of switching device. Changes in the

bus/section groups leads to changes into the reference system network and topology

processor updates this changes .See figure 2.3 [2].

Figure 2.3: Bus/section model [2].

2.3.2 Bus/branch model

Substations have brunch device i.e. transmission lines, transformers, phase shifters, and

series devices, shunt devices i.e. capacitors, reactors, synchronous condensers, static

VAR compensators loads and generators etc. Substations also have metering device

i.e. power and current flow meters, power and current injection meters and voltage

magnitude meters. In bus/brunch model describes how this device are connected in the

power system network[2].

Figure 2.4: Bus/branch model [2].


2.3.3 Distributed Topology Inference processor

The process of arriving at some conclusion about connectivity of the electrical network

from series of time- stamped process measurements. This time stamped process mea-

surements (i.e. positive sequence current), called a fingerprints. One station is associated

with one or more bays in the network. Connectivity of bays is determined from time

series correlations and mean differences of fingerprints. Strength of the connectivity is

defined by a relation between correlation and mean difference of the fingerprints, called

an incidence certainty.

The topology inference concept is based on the paper [3], topologies are determined by

collecting both analog and digital measurements from local levels. A structured way of

information exchange in between all substations in the local level can be used to correlate

with connected bays. The collected data from local bays can be used in the distribution

level in the same way among the substations. Therefore topology of correlation in both

local levels and the distribution network becomes inferred 2.5 [3].

2.4 Decentralized Topology Inference System Architecture

The system has three main components are as follows,

• Multi Agent System

• IEC 61850 standard for Substation Automation Specification (SAS)communication

• Overlay Network

Figure 2.5: Topology inference system architecture [3]


2.4.1 Multi Agent System

Multi agent provides the way of using more than one software agent. This proposed

distributed topology inference system platform is MAS based. Agent of MAS can com-

municate between the subsystem and also the individual components (agents) among

the subsystems.

2.4.2 IEC 61850 standard for Substation Automation Specification

(SAS) communication

IEC 61850 standard for Substation Automation Specification (SAS) communication.

This standard specifies a set of communication protocols for exchanging information to

process level i.e IEDs and also determining the local structure bays. This standard of

communication can also be used by MAS.[11][12][13]

2.4.3 Overlay network

An overlay network is assumed to provide value added communication features such as

reliable delivery, encryption, authentication, announcing of nodes entering or exiting the

network to satisfy the information exchange requirements for topology inference method

as shown in figure 2.5 [3][14][15][16].

2.4.4 Methodology

This section will describe the methodology of collecting information about the electrical

topology of an electrical network.

The topology of inference concept is the ability of a substation agent to determine the

local connectivity of its own station from the local SAS. The station has one or more

incoming lines and or one or more outgoing feeders. The station agent will exchange

time stamped process measurements with other station agents to infer the connectivity

of electrical network shown in figure 2.5 [3].

To understand the plug and play functionality of an agent assumed that a station agent

has no information about its own connectivity or the electrical network that the sub-

station agent is part of. Station agent starts queries the local SAS using Manufacturing

Message Specification (MMS) queries as specified in IEC 61850-8. If the Substation

Configuration language (SCl) is accessible by substation agent than it can distinguished


between bays and voltage levels to categorize available process data for inference topol-

ogy processor2.5 [3].

A newly started agent initiated with local information of substation name, voltage level

and status of each bay in the substation will announce its presence to other agents

through the overlay network2.5 [3].

Each substation agent maintain a table of incidence certainty matrix.Number of row is

the bays of incoming lines and or outgoing feeders. Each bay of that substation agent

has a row in the matrix with number of combination of all other bays of the electrical

network that with some probability could be connected to the local bay. This probability

to be connected with other bay is called incidence certainty 2.5 [3].

Incidence certainty matrix is created by taking time stamped process measurements

called a fingerprints. A fingerprints of a bay is sent a query message to other station

agents on the overlay network . When query message of other bays (fingerprints ) are

received by local station agent than the incidence certainty for each combination(two

fingerprints ) is calculated from their time series time series correlation, is the incidence

certainty . For connected bays correlation will be stronger or close to 1 and for the

unconnected bays correlation will be weaker than connected bays correlation. Finally a

incidence certainty threshold will be defined in the incidence certainty table to determine

the topology of electrically connected network 2.5 [3].

2.5 Related work

In [3] ”describes a methodology and system architecture for determining the electrical

topology by using process and model data from IEC 61850 complaint substation devices.”

This master thesis is a continuous work of previously presented work of that paper in

order to validate the methodology with IEEE reference network.

Chapter 3

Reference Model Selection

This chapter describes the selection of reference electrical network for validation of topol-

ogy inference application. There are different kind of electrical network available de-

pending on the voltage level and structure of the network. Here distributed topology

processor needs a reference electrical network in distribution level. There are few aspects

that need to consider before choosing the right reference electrical distribution network.

Whether the electrical network would be radial or meshed, standard of acceptance, scope

of distributed generation unit in the network and the voltage level of distribution net-

work? Above all a selection of right distribution network is important so that it can be

built in Simulink platform and critically analyzes the topology of inference with worst

case scenario. Before selecting the reference electrical network some of the electrical

networks were considered that are presented below.

3.1 IEEE 30 bus electrical Network

The IEEE 30 Bus electrical network represents a portion of the American Electric Power

System in the Midwestern US as of December 1961. The data for load flow analysis,

dynamic analysis can be found in reference IEEE Common Data Format by [17]. Ac-

cording to structure of Swedish electrical power system described in Chapter 1 this

electrical network is belongs to sub-transmission level voltage ranges 132 Kv-33 kV. It

has 30 Buses, 41 brunches, 6 Generators, 21 loads, and 2 shunts and 4 Transformers in

one area [18]. See here3.1.

13

Chapter 3. Reference Model selection 14

Figure 3.1: IEEE 30 Bus electrical network

[17] [18].

3.2 IEEE 34 Node test Feeder

IEEE 34 Node test feeder is an actual feeder located in Arizona, US. This distribution

model can be found from Distribution System Analysis Subcommittee IEEE 34 Node

Test Feeder document. This radial distribution network consists of 34 buses, 25 loads

including 6 spot loads, 2 shunt capacitors and 2 regulators. This distribution network

is operated at 24.9Kv to 4.16KV. It has two voltage regulators, two capacitor banks

[19][20].

800

806 808 812 814

810

802 850

818

824 826

816

820

822

828 830 854 856

852

832888 890

838

862

840836860834

842

844

846

848

864

858

Figure 3.2: IEEE 34 Bus Network

[19][20].


3.3 IEEE 118 Bus

The IEEE 118 Bus Test Case represents a portion of the American Electric Power System

(in the Midwestern US) as of December 1961. The data for load flow analysis, dynamic

analysis can be found in reference IEEE Common Data Format by Rich Christie at the

University of Washington. IEEE 118 Buses network is meshed for voltage level of 11

KV with 186 brunches, 54 Generators, 99 loads, and 14 shunts and 9 Transformers in

one area. This electrical distribution network is of 3 zones and building this network

in Simulink platform needs fair amount time and work and could beyond the runtime

memory for analysis [21].IEEE 118-bus, 54-unit, 24-hour system Unit and Network Data

Zone 1 Zone 2 7 2 13 33 43 44 54 55

1 117 45 56

15 34 53 3 12 14 46 57 36 52 6 11 17 18 35 47 37 42 58 4 16 39 51 59 19 41 48 5 40 49 50 60 38 8 20 9 30 31 113 73 66 62 10 29 32 21 69 67 61 65 64 28 114 71 81 26 22 75 118 76 77 115 68 80 63 25 27 23 72 74 116 24 98 99 70 78 79 97 87 86 85 88 96 90 89 84 83 82 95 112 91 93 94 107 106

92 106 109 111 100 105 103 104 102 101 108 110 Zone 3

Fig. 1. The 118-bus system

TABLE 1 GENERATOR DATA U Bus

No. Unit Cost Coefficients Pmax

(MW) Pmin (MW)

Qmax (MVAR)

Qmin (MVAR)

Ini. State (h)

Min Off (h)

Min On (h)

Ramp (MW/h)

Start Up

(MBtu)

Fuel Price

($/ MBtu)

a (MBtu)

b (MBtu/ MW)

c (MBtu/MW2)

1 4 31.67 26.2438 0.069663 30 5 300 -300 1 1 1 15 40 1 2 6 31.67 26.2438 0.069663 30 5 50 -13 1 1 1 15 40 1 3 8 31.67 26.2438 0.069663 30 5 300 -300 1 1 1 15 40 1 4 10 6.78 12.8875 0.010875 300 150 200 -147 8 8 8 150 440 1 5 12 6.78 12.8875 0.010875 300 100 120 -35 8 8 8 150 110 1 6 15 31.67 26.2438 0.069663 30 10 30 -10 1 1 1 15 40 1 7 18 10.15 17.8200 0.012800 100 25 50 -16 5 5 5 50 50 1 8 19 31.67 26.2438 0.069663 30 5 24 -8 1 1 1 15 40 1 9 24 31.67 26.2438 0.069663 30 5 300 -300 1 1 1 15 40 1

10 25 6.78 12.8875 0.010875 300 100 140 -47 8 8 8 150 100 1 11 26 32.96 10.7600 0.003000 350 100 1000 -1000 8 8 8 175 100 1 12 27 31.67 26.2438 0.069663 30 8 300 -300 1 1 1 15 40 1 13 31 31.67 26.2438 0.069663 30 8 300 -300 1 1 1 15 40 1 14 32 10.15 17.8200 0.012800 100 25 42 -14 5 5 5 50 50 1 15 34 31.67 26.2438 0.069663 30 8 24 -8 1 1 1 15 40 1 16 36 10.15 17.8200 0.012800 100 25 24 -8 5 5 5 50 50 1 17 40 31.67 26.2438 0.069663 30 8 300 -300 1 1 1 15 40 1 18 42 31.67 26.2438 0.069663 30 8 300 -300 1 1 1 15 40 1 19 46 10.15 17.8200 0.012800 100 25 100 -100 5 5 5 50 59 1 20 49 28 12.3299 0.002401 250 50 210 -85 8 8 8 125 100 1

Figure 3.3: IEEE 118 Bus Network

[21].

3.4 Medium Voltage (MV) Distribution Network

The MV network is a real network from rural case network with two distinct areas with

different voltage levels of 30 KV and 15 Kv. The system has 6 micro grids, 3 Distributed

generator units shown in 3.4

3.5 Swedish LV Distribution Network

Below there is two different Swedish LV distribution networks 3.5 and 3.6 used by Vat-

tenfall Eldistribution AB. In the first model is of 19 buses nominal three phase voltage

level is 400V and the transformer rating is 10/0,4KV, rated apparent power is 200KVA.

The larger model is of 74 buses, nominal three phase voltage level is 400 V, transformer

rating is 22/0,4KV and the apparent power is 630KVA.


Figure 3.4: MV distribution Network.

Figure 3.5: Swedish LV Distribution Network

3.6 IEEE RBTS distribution system

Reliability test system (RBTS) Bus 4 and bus 2 is the classical model in electrical

distribution network for reliability analysis and recommended and approved by the IEEE

Power engineering Society for educational purpose. These models are sufficiently large

enough that practical factors can be realistically modeled for assessment but sufficiently

small that sensitivity analysis can be easily understood. RBTS 4 network has 4, 33KV

ring circulated network around the distributed network. Distribution voltage levels are

11Kv and 400V which is supplied from 7 feeders by 6 33Kv/ 11Kv transformers. Further

distribution of the supply is done by 11 KV switchgear. The distribution system has

both high voltage and low voltage customers. The 0.425KV low voltage customers

are supplied via 11/0,415 KV transformers and the 11 KV consumers are connected

directed. 33 KV site has 100% availability. Dotted lines are for distributed generation

units. When the distributed generation units are connected the RBTS network becomes

meshed otherwise its a radial network. See in figure 3.7 [6][22].


Figure 3.6: Swedish LV Distribution Network

RBTS bus 2 also meshed network with similar voltage range and dotted lines could

be connected for distribution generation units but comparatively small network than

RBTS bus 4. The distribution system for bus 2 is supplied by two33/11 KV, 16MVA

transformers. Further distribution of the supply is done by 11 KV switchgear. The

distribution system has both high voltage and low voltage customers. The 0.425KV low

voltage customers are supplied via 11/0,415 KV transformers and the 11 KV consumers

are connected directed. 33 KV site has 100% availability. See in figure3.8 [6] [22].

3.7 Selection

Reliability test system Bus 4 has chosen from the above mention electrical distribution

network. The model is sufficiently large enough that practical factors can be realisti-

cally modeled for assessment but sufficiently small that sensitivity analysis can be easily

understood. This model has the same voltage level as the distribution level and can be

used with distributed generation unit by following previously work on this same paper.

Another reason is model gives both meshed or radial features of electrical distribution


Figure 3.7: IEEE RBTS distribution system of Bus 4

[6] [22].

Figure 3.8: IEEE RBTS distribution system of Bus 2

[6] [22].

network with or without distributed generation units respectfully. So, distributed topol-

ogy of inference application this reference could be the best choice.

Chapter 4

Algorithm

This chapter briefly discuses about the algorithm for the DTIA based on Multi Agent

platform that infer the topology of the distribution network by collecting time stamp

samples from substation agents to communicate each other and define the connectivity

of the electrical network. Lets assume, at first a substation agent has no information

about the electrical network and the other neighboring substation agents [4]. In each

substation agents has the following capabilities are as follows,

• Managing a friend list

• Maintaining the matrix of bays

• Handling incidence certainty queries

4.1 Managing a friend list

When one agent of the application can exchange information with other agents is called

friends. Friends can be electrically connected or not. Friends of the other bays of the

electrical network of the reference model can inference each other buy updated relevant

information i.e. name, overlay network address, time of last information exchange,

geographical location. List of bays and bay specific information such as voltage level

and updated status of breaker [4].

In the figure basically shows three main steps. At first interrogation is performed at the

local SAS devices for its local substation structure and functionality and this process is

done by using MMS queries defined in IEC 61850-8-1. After this queries all SAS devices

are split into its Logical nodes (LN) into a tree data structure from which each voltage

and all other measurements can be derived [4].

19

Chapter 4. Algorithm 20

Next step is when a substation agent knows its local context than it stars to announce

itself to all other friends using broadcast or a directory of listening structure. Here

different type of communication overlay can be used [4].

The third step is to maintain a continuous update of each agents status and also to

detect new arrival friends. This also needs filtering capabilities because a agents doesnt

need to store information about the friends with no bays or outside of its graphical area

with a compatible voltage level. A compatible voltage can be determined by using IEC

61850 data model. Voltage level must have to be the same compatible [4].

A reasonable geographical area can be defined by the maximum area that an agent can

interact with other agents. And this area is depend on several factors i.e. bandwidth

available for the agent, memory of the agents, size of the network etc shown in figure

4.1 [4].

Figure 4.1: Understanding local context and managing friends list [4].

4.2 Maintaining the matrix of bays

When a new station agent is created than it has a single matrix containing only with

list of local bays. As the agent starts to communicate with other agents of the incidence

matrix, the agent can send incidence certainty request to other friends with bays who has


the same compatible voltage level and also in the same geographical area. Thus the two

friends becomes neighboring candidate and a fingerprint has established in the certainty

matrix. After becoming the neighboring candidate and establish a fingerprint they starts

communicate through the compatible bays. On the other hand if the queries find the

incidence certainty is above the incidence certainty threshold than that agent will not

send any further incidence certainty request. Thus by using fingerprint and incidence

certainty request possible to minimize the load on the over lay network. Another reason is

in the distribution network electrically connected substation agents are of same voltage

level. If no neighbor can be found for a bay after query request process, the station

agent will than monitor by the local process measurements for large changes. Incidence

certainties may change its value over time by status changes of agents observed by

measurements. If incidence certainty of a specific bay is bellow its threshold limit than

the last known neighbor is queried before starting radial query process in its geographical

regional [4].


Figure 4.2: Populating and updating the incidnece certinity matrix [4].

Chapter 5

Simulation System Architecture

This chapter briefly describes the architecture of the interface between the electrical

reference network and the DTIA built in the Java platform. Five main ways has been

considered to interconnect the electrical reference network with DTIA based on how the

signals are fetching from the network are as follows .

• Off-line Model

• Real Time Interface Architecture

• Real Time Interface with JAVA API

• Real time interface with matlabcontrol

• Real time interface with TCP/IP or UDP/IP communication

The reference electrical network can be build in Simulink platform by using windows

or mac operating system but for real time simulator i.e. OPAL-RT where needs a few

additional blocks to perform simulation in target machine which is in Linux platform.

If the signals are fetching from electrical network running in real time simulator target

machine(which is not the same operating system where the electrical reference network

were built ) then real time lab OPC server could could be in terms of industrial relevant

and demonstration way but bit complex to configure or synchronized with the application

to Network. On the other hand reference model can be interface with algorithm in off

line mode. Offline mode the electrical network can be run and built from the same

operating system but in that case total interfacing system will lose industrial relevant

and demonstration. Here all five interfacing ways are discussed.

23

Chapter 5. Simulation System Architecture 24

5.1 Off-line Model

Off-line model interfacing is based on off-line model of the electrical network built in

Simulink with topology of inference algorithm by using matlabcontrol Java API tools

.Here matlabcontroel API tools is used to communicate between the DTIA and the

electrical distribution network, considered to be the first analysis step before starting any

industrial or rather complex interfacing structure of validate the methodology because

it can be perform in the same computer. For this master thesis electrical distribution

network was built in Simulink and the DTIA was built in the Java platform As shown

in figure 5.1 DTIA can also be built in JADE or JACK and matlabcontrol API tools

needs to put in the class path to call from the main program. Here matlabcontrol API

tools is an additional tools used to perform necessary commands in matlab from DTIA

that needs to infer the topology.

Figure 5.1: Offline Model.

5.2 Real Time Interface Architecture

Real Time -LAB.OPC Server is compatible with RT-LAB, that respects Data access from

the model running in real time target machine , allows to interface common automation

and supervision tools with RT-LAB. Using RT-LAB.OPC Server, reference model can

manage and control the simulation by accessing the parameters in real-time for read

or write operations. It also used to RT-LAB.OPC Server offers the possibility to use

RT-LAB simulator along with industrial supervision software and user-specific solutions.

This is the most interesting interfacing way for industrial relevant and demonstration

purpose that this master thesis emphasis on interface between electrical network and


received the signal by Jeasy OPC client . The validation of DTIA will be much more

acceptable from Industrial prospects to accept this application.

In OPAL-RT OPC server the required signals or parameter from the real time electrical

distribution network can be mapped by an XML file to receive by the Jeasy OPC Client

to use it from the DTIA. In the case of real time simulation time stamp samples are

mapping in the OPC server are changing in real time. So, the application built in

Java platform has to be different way to fetching real time stamp samples and to use

it properly. Thus a industrial solution can be done. Figure 5.2 shows the real time

interface algorithm.

Figure 5.2: Real time Interface Algorithm.

5.3 Real Time Interface with JAVA API

Python API is the another way to interface with real time(OPAL-RT) electrical distri-

bution network essential from the Java based application DTIA shown in figure 5.3. To

implement this interface architecture OPAL JAVA API tools compatible with RT-LAb

can be used to interface between reference electrical network running in the real time

target machine and the algorithm DTIA built in JACK or Jade based Java platform.

It is a challenge in any case - the method is to use the RT-LAB Java API to directly

access signals from the simulation. This configuration doesn’t require any OPC server

but loses in terms of industrial relevant and demonstration way .


Figure 5.3: Real Time Interface with JAVA API.

5.4 Real time interface with TCP/IP or UDP/IP commu-

nication

Another way of interfacing the electrical distribution network running in real time tar-

get with algorithm DTIA by using the TCP/IP or UDP/IP communication protocol.In

RT lab allows using TCP/IP or UDP/IP communication protocol. So it is possible to

send time stamp sample measurements from real-time electrical network to the appli-

cation and receive command from DTIA . Configuration of TCP/IP or UDP/IP could

be complex task to deal with many time stamp samples coming from distribution net-

work.Again this configuration will lose in terms of industrial relevant and demonstration

way to validate the DTIA as shown in figure 5.4.

Figure 5.4: Real time interfacing with TCP/IP or UDP/IP communication.


5.5 Real time interface with matlabcontrol

Real time interface with matlabcontrol is similar to the interface with off-line validation

but in this case electrical distribution network will run in real time. The electrical

distribution network was built in Simulink based RT-Lab for real-time validation of the

algorithm and the simulink is matlabcontrol compatible. So, matlabcontrol could also

be used to interface with DTIA algorithm JAVA platform. See figure5.5.

Figure 5.5: Real time interfacing with matlabcontrole.

5.6 Validation of Offline Model

For distributed topology inference application different current signals were considered

from buses in the electrical distribution network. Detail modeling of reference network

has described in chapter 6. This time stamped samples currents are coming from all bays

of bus section of the electrical distribution network are the process measurements to infer

the topology of the electrical distribution network by using the algorithm DTIA. This

time stamped sample process measurements obtained from positive sequence current

has sent to its named work space mat file. Two xml files is created for all station

list of the reference network and another one is for all connected bays of the reference

network.By using this xml file to extract the values in each step time of simulation from

its named bays to use it in topology processor for decision making inference . Once all

the connected bays are distinguished from the unconnected bays from the station bays

xml trees a new command has send to matlab to plot how strongly they are correlated

in each connected bays . In Analysis part can be found in details about it.


5.7 Validation of Real time interfacing

The main advantages of the real time reference model is that the model can run in real

time. So the performance can be analysis for longer duration of time compare to off-line

validation. Thus the obtained analysis result will become more acceptable .

RT-Lab OPC-Server is compatible with Rt-Lab and allows to access of running the

reference model in the target . Figure 5.8 shows the pop up window of RT-Lab OPC-

Server that needs to configure to get the signals or the parameters from the running

electrical network in OPAL-RT.RT-LAB version is .... .File name tab is to put the

path of the electrical network from the workstation. There is two way of fetching signal

either select all the signal or by mapping the required signal in an XML file and put the

XML file path in mapping file tab. Select the dynamic acquisition tab and the set the

value one by one. Finally all this configuration needs to save before run the OPAL-RT

OPC server. The mapping signal can be fetched after load and executing the electrical

network in the RT-LAB. When the model is running in OPAl-RT than select the run tab

in the OPC pop up window . In the workstation of OPAL-RT there is a OPC SERVER

client called NI server explorer can be used to see the signals are fetching properly by

the OPC server . In the SM Systme block of Simulink model inside the red mark block

Figure 5.6: RMS current block in simulink..

there are RMS block can be found the second block has designed like this to map for

OPC server that is the simplest way to get the rms value. See figure 5.7


Figure 5.7: RMS current block in details.

For DTIA application the signals generating from the reference electrical network exceeds

the maximum number of signals that RT-LAb OPC-server. RT-LAb OPC-server can

fetch 1000+ signals and all the demo models given for the RT-LAb OPC-server are

examples bellow that range. But the reference model has approximate 5000+ signals

therefore it crashes when one try to run the model in real time. Therefor there needs to

mapped the desired signal instead of selecting all signal as shown in RT-LAb OPC-server

5.8 pop up dialogue window.


Figure 5.8: OPAL-R OPC Server

Chapter 6

Implementation

6.1 Introduction

This chapter describes the modelling of RBTS electrical distribution network in SimPower-

System. Before describing the simulation part details, perimeters and description will

be presented bellow.

Reliability test system Bus 4 was chosen from the IEEE paper [6]. The RBTS bus- 4

distribution network is the part of a transmission network connected at bus 4 shown in

figure 6.1. It describes the model with all the data that are needed to perform basic

reliability analysis. The model is sufficiently large enough that practical factors can be

realistically modeled for assessment but sufficiently small enough to execute in real time

on the simulator.

6.2 Description of the RBTS Bus -4 Distribution Network

Bellow the system summery of reference can be found in table 6.1. Length of the feeder

section are given in the table 6.3. This lengths data are used for simulation[6].

6.3 Load and DG Profile

For dynamic loads and the distributed generation units, distribution of active and re-

active power needs to be parameterised externally from mat file. In RBTS has five

different type of load profile. The customer load data are shown in the table 6.4 taken

from the [6] of RBTS network. According to the table the reference has five different

31

Chapter 6. Implementation 32

Figure 6.1: Single line diagram of RBTS [5]

Table 6.1: System Summary Component[6]

Component No How much ? P(MW) Q(MVAr)

Buses 68 Total Gen Capacity 100.0 -140.0 to 200.0Generators 1 On-line Capacity 100.0 -140.0 to 200.0CommittedGenerators

1 Generation(actual) 42.0 4.1

Loads 38 Fixed 40.2 0.0Branches 67 Losses(I2 ∗ Z) 1.80 4.06

Transformers 0 Branch Charging (inj) - 0.0Inter-ties 0 Total Inter-tie Flow 0.0 0.0

Areas 1

type of load. These different load types are connected at the end of different bays that

mention in the table. These same type of load will be different in real electrical network

consumed by different load. Therefor the load profiles has to be different and dynamic

because in real life electrical scenario loads cannot be exactly equal even though the load

type is same here unless there is an artificial chaos in the system. Here both static loads

and dynamic loads are considered for the different type of analysis for the DTIA. The

Different load type are choosing 80 - 90 % of its peak value. For dynamic analysis of

the DTIA some 0.5% to 1% Gaussian noise are added with the dynamic loads to make

these load. In section 6.5.7 describes how Gaussian noise are added in to the loads and

generators [6]. The parameter of DG are teken from [23].


Table 6.2: System Summary Voltage and Power [6]

Minimum Maximum

Voltage Magnitude 0.923 p.u. @ bus 13 1.000 p.u. @ bus 1Voltage Angle -8.87 deg @ bus 13 0.00 deg @ bus 1

P Losses(I2 ∗R) - 0.15 MW @ line 1-33Q Losses (I2 ∗X) - 0.35 MVAr @ line 1-33

Table 6.3: Feeder types and Lengths [6]

feeder typelengthKm

feeder section numbers

1 0.62 6 10 14 17 21 25 28 30 34 38 41

43 46 49 51 55 58 61 64 67

2 0.751 4 7 9 12 16 19 22 24 27 29 32 3537 40 42 45 48 50 53 56 60 63 65

3 0.83 5 8 11 13 15 18 20 23 26 31 33

36 39 44 47 52 54 57 59 62 66

Table 6.4: Customer Data [6]

number ofload points

load pointscustomer

typeload level per load point, MW

number ofcustomers

average peak15 1-4,11-13,18-21,32-35 residential 0.545 0.8869 2207 5,14,15,22,23,36,37 residential 0.500 0.8137 2007 8,10,26-30 small user 1.00 1,63 12 9,31 small user 1.50 2.445 17 6,7,16,17,24,25,38 commercial 0.415 0.6714 10

Total 24.58 40.00 4779

6.4 Power system modelling

In order to validate the methodology of DTIA the next task was to model the electrical

distribution network which can give the time stamp sample value of current for the anal-

ysis. The electrical distribution network was built in MATLAB/SIMULINK platform.

SimPowerSystems software comes with Simulink software is a popular tool to model

electrical, mechanical, and control systems. SimPowerSystems software allows the user

to build powers system model by using the Simulink environment. Simulink library has

all typical model of power equipment i.e. transformers, lines, machines, and power elec-

tronics. The reason of modelling the distribution network in Simulink platform is simple

like click and drag procedures. Simulink uses Matlab computational engine and users

can uses matlab toolbox to receive and sending linier or non linier signals in continuous

or as discreet time stamps. Another reason of using SIMULINK that the model can be


easily convert into real time because in real-time platform OPAL-RT environment also

in SIMULINK based platform.

6.5 List of SimPowerSystems Blocks Used

Bellow the simulink block has used during the simulation are as follows,

• Three phase Programmable Voltage Source

• Three phase two winding transformer

• Three-Phase V-I Measurement

• Three-Phase Breaker

• Three-Phase PI Section Line

• Three-Phase Dynamic Load

• Gaussian Noise Generator

• Powergui

• Discrete 3-phase Positive-Sequence Active & Reactive Power

6.5.1 Three phase Programmable Voltage Source

This block generates three phase sinusoidal voltage with time varying parameters. For

this electrical distribution network three phase Programmable Voltage Source block

used an external infinite voltage source. The figure 6.2 shows the block used in the

model. First tab is to define the phase to phase rms voltage amplitude , phase angle and

frequency.Second tab is to select the time variation in none, voltage amplitude, phase

angle or frequency . In this model this tab kept none as there is no time variation is

required. Fundamental and/or Harmonic generation tab kept uncommented.

Input and Output

Terminal denoted by “n” is connected to ground with an external ground block . The A

B and C terminals represents the three phases of voltages . By using this block variation

of amplitude , phase and frequency of the fundamental can be programmed.


Figure 6.2: Three phase Programmable Voltage Source

6.5.2 Three phase two winding transformer

The three phase two winding transformer block shown in left figure 6.4 implements a

three-phase transformer by using three single-phase transformer. Winding 1 ad Winding

2 connections represents the primary and secondary side of the transformers. Primary

side is connected as Y connection so that the voltage seen from three phase voltage source

block and in primary winding side are same. Secondary winding 2 connection kept as Y

connection to make the turn ratio simpler. Because calculations becomes more compli-

cated if the connection arrangement is Y-Delta for the correspondence transformation

voltages. Saturable core tab kept uncommented because if selected than it implements

saturable three-phase transformer.

The figure 6.4 is to define the parameters of the transformers. The tab Units kept pu

for load flow analysis. Next tab is to define Nominal power and the frequency. Nominal

power the reference electrical network is 100MVA for the 33KV/11Kv transformers and

1MVA for the 11KV/400V transformers. The frequency is 50HZ as of EU power system

. The phase to phase voltage of primary and the secondary side were set as per reference

electrical network parameters. The transformer resistances and reactances are chosen by

default. Magnetizing inductance and resistance are generally very high and here kept as

default value. In the Advanced tab shown in figure 6.5 Break Algebraic loop in discrete

saturation model was selected to speed up the simulation.


Figure 6.3: Three phase two windingtransformer(Parameters)

Figure 6.4: Three phase two windingtransformer(Configuration)

Figure 6.5: Three phase Pro-grammable Voltage Source(Advanced)

Figure 6.6: Three-Phase Breaker


Input and Output

Winding 1 connection sets are for ABC terminals and Winding 2 connection sets are for

abc terminals.

6.5.3 Three-Phase V-I Measurement

This block is used to measure instantaneous three-phase voltage and current measure-

ments in a circuit shown in figure. This block is connected in between two other Sim-

Power Blocks in the network shown in ??.

6.5.4 Three-Phase Breaker

The Three-Phase Breaker block used a three-phase circuit breaker in the network shown

in the figure 6.6 where the opening and closing times can be controlled either from

an external Simulink signal (external control mode), or from an internal control timer

(internal control mode).

The Three-Phase Breaker block uses three Breaker blocks connected between the inputs

and the outputs of the block. You can use this block in series with the three-phase

element you want to switch. See the Breaker block reference pages for details on the

modeling of the single-phase breakers.

6.5.5 Three-Phase PI Section Line

The Three-Phase PI section line block was used for all the distributed lines in the

distribution network. This block implements a balance three-phase transmission line

with parameters lumped in a PI section.

6.5.6 Three-Phase Dynamic Load

This block implements a three-phase dynamic load.Dynamic load is varying by using the

external control of PQ from workspace array. As shown in figure 6.8 this block needs

to set nominal voltage level according to the configuration of the reference distribution

network. The frequency was set to 50 HZ. In the field of Active and reactive power at

initial voltage and Initial positive-sequence voltage and phase angle was set according

to the specification of load flow specification for the reference model shown in Appendix

part.


Figure 6.7: Three-Phase PI SectionLine

Figure 6.8: Three-Phase DynamicLoad

6.5.7 Gaussian Noise Generator

The Gaussian Noise Generator block generates Gaussian noise with given mean and

variance values. Gaussian Noise generator block is added with the dynamic load profile.

For static analysis of DTIA no Gaussian noise are added , only 1 to 2% Gaussian noise are

added for stochastic or dynamic analysis of DTIA. “The Initial seed parameter initializes

the random number generator that the Gaussian Noise Generator block uses to add noise

to the input signal.” Different ”randseed ” is used for each block to initializes the seed

. See figure 6.14[24].

6.5.8 Powergui

For power system model build in Simulink Powergui block is necessary for simulation

and needs to place in the build model.Powergui block has two part , first part is the

Simulation and configuration options and the second part is the analysis tools.

From the first part of Powegui block shown in 6.9 need to select whether the model

should run either in continuous, Discrete or phasor method. For the application of

topology implementation the reference model simulation needs to run in Discrete with

standard sample time of 50 micro seconds as shown in figure 6.10. In load flow tab

the frequency was set to 50Hz , base power 100MVA , PQ tolerance 0.0001(pu) was

by default. Maximum iteration for load flow analysis was 50 (by default). Voltage and


Power unites are KV and MW respectively shown in figure 6.11. Display SimPowerSys-

tems warnings and messages was selected which enable the warning message during the

simulation. Start simulation with initial electrical states from blocks. Restore disabled

Figure 6.9: Powergui block

Figure 6.10: Powergui(solver)

links of SimPowerSystems blocks were set to warning to display during the starting time

of simulation.

In the second portion of the powergui block for the analysis part are described bellow,

State Voltages and Currents :This tool dialog box provides the steady-state voltages

and currents of the model.

Initial States Setting : This tool dialog box is to set the initial condition i.e. Zero or

steady state.

Load flow: This tool dialog box is to perform load flow and initialize three-phase

networks and machines.

Machine Initialization: Machine Initialization dialog tool box is to initialize three-

phase machines of the network.

Use LTI Viewer: This tool dialog box is used to find the state space model and this

was not used for this network.


Impedance vs Frequency Measurement: This tool dialog box is used to measure

impedence vs frequency for the point where impedance block is used in the model ,

however this tool box were not used for this thesis work.

FFT Analysis: This tool box is used to perform Fourier analysis of signals stored in a

structure with time format. It was not used for this project .

Generate Report: This tool allow to generate a report of steady state variables ,

initial states, and machine load flow models.

Hysteresis Design Tool: This tool box is used when there is a saturable Transformer

block in the network and was not used in this project.

Compute RLC Line Parameters: This tool box is used to find R;L;C components

of the transmission lines from its conductor characteristics and tower geometry.

Figure 6.11: Powergui(Load Flow)

Figure 6.12: Powergui(Preferences)

6.5.9 Discrete 3-phase Positive-Sequence Active and Reactive Power

This block is used to measure the three-phase active power P and reactive power Q

associated with a periodic set of three-phase voltages and currents which may contain

harmonics shown in figure 6.15. But for the DTIA here only three-phase current were

used by another block called Discrete RMS block(measure the root mean square value

of the fundamental component of the signal in discrete sample time)

Input and Output

Vabc input: Three-phase instantaneous voltage (V) Iabc input: Three-phase instanta-

neous Current (I)


Figure 6.13: Powergui(Machine initialization tool)

Output 1: Vector [Mag V(V) Mag I(A)] of the fundamental values of the voltage &

current (peak values of the positive-sequence) Output 2: 3-phase PQ measurement vector

[P(W) Q (var)] evaluated at the fundamental frequency.

6.6 Naming of each bays of the reference network

Naming of each bays of the reference network: This section describes the name / ID

of each bays that carries the unique identity for whole analysis. For topology inference

application we only use the data measurements of each bays from 11KV side after the

transformer from 33 KV side. In Supply point SP1 has three feeders to F1, F2 and F3.

So, the name of the station agent will be SP1 F1, SP1 F2 and SP1 F3 respectfully. Each

station agent can have more than one bay.As the agents mention earlier are the down

side of the transformer so the name we choose SP1 F1 Dn, SP1 F2 Dn and SP1 F3 Dn

respectfully. At the same way other feeders coming from SP2 and SP3 are follows similar

naming pattern. After this Supply point agents in each feeder starts from Feeder name

F1, F2,...F7. Each agent has several bays and the name depends on connecting bays or

load or DGs. I.e. bays towards to load point LP2 through line 4 named as F1 D2 4.

Here D indicates the distribution point sequence under Feeder. If there is only DG is

connected and no line is connected in that case bay name has given like F1 D3 WT1. So,


Figure 6.14: Gaussian Noise Gener-ator

Figure 6.15: Discrete 3-phasePositive-Sequence Active and Reactive

Power

from this name one can find this bay it is under station name F1 distribution point D3

and the agent is connected to WT1. Figure shows 6.16 details naming for the application

.

6.7 RBTS Network build in SimPowerSystem

The main reasons of modelling the RBTS network in this study is to test the DTIA

for different scenarios. The scenarios were the different generation profiles for active

distribution generation units and the different load shading performance in the network

to define the topology from the DTIA. Different blocks were required to integrate the

build model to DTIA. Below in figure 6.16 shows RBTS bus -4 network in online diagram.

Green generations are the DG as per [23].


Figure 6.16: RBTS Network build with naming tag

6.8 Model Validation

6.8.1 Machine Initialization

All the machines and loads were initialize from the machine initialization tool box in the

Powergui block.The machines and loads are need to be parameterised in the model block

according to the reference model. A bus-section model of RBTS-bus 4 with all loads

details of all initialization loads and machines had checked is attached in the appendix.

6.8.2 Steady-state voltages and currents

When all the machines, loads, lines are parametrized properly then steady-state voltages

and currents analysis had performed and found voltages and current in reasonable range

shown in figure 6.18.

6.8.3 Load Flow

Load flow analysis has performed in RBTS model by removing all subsystem VI mea-

surements block subsystem and the subsystem of dynamic load blocks. External control


Figure 6.17: RBTS Network build in SimPowerSystem

of P and Q also removed from all dynamic loads. Any SimPowerSystem block using in

subsystem in the reference electrical model cant perform load flow calculation. In ap-

pendix section machine initialization shows all information of the synchronous machines

and the dynamic load. From the powergui block machine initialization tab machines and

loads were configured and updated the model. Than in the load flow tab is for compute

the load flow for the electrical model. Load flows were performed for voltage exponent

npnq represents constant current characteristic.


Figure 6.18: Steady-state voltages and currents

6.8.3.1 Machine Initialization

The load flows from the matpower and the load flows of build model are presented in the

appendix are of different sequence. In the matpower load flow presented in bus sequence

wise but in the build model load flows performs in its own sequence according naming

tag in the VI block. Below several bus load flows are compared with load flows obtained

from RBTS case load flow of matpower. In the matpower load flows for DG units were

not considered, whereas build model has distribute generation units. Figure 6.19 shows

the pop up window of load flow.

6.9 Offline Simulation interface

For topology inference application positive sequence value of current magnitude were

considered that needs to be collected from existing bays of all stations. Details of

simulation system architecture can be found in section 5.6. All this bays were named as

discussed above to make proper use of this value by application. This sample values were

then send to corresponding work space mat file to store for each decimation time steps

according to their names. Decimation is defined as a variable ”dc” in all corresponding

current measurements work space and initialize ”dc” variable either initialize tool box or

work space (i.e. dc400 for 50Hz sampling currents) In offline simulation runs at sample

time of 50 micro second timestamps. So, each seconds simulation has 20,000 steps. Any

samples of current measurement can be collect as a fraction of 20,000 simulation steps

and decimation variables ”dc”.


Figure 6.19: Load flow

6.10 Real time Simulation interface

For real-time simulation RT-LAB tools is used and some additional RT-Lab simulink

block needed during the real-time simulation are described bellow, section 5.7

• RT-LAB

• OpComm block

6.10.1 RT-LAB

RT-LAB is a tools for running the simulations of models build in SIMULINK based plat-

form with some additional blocks in the model on a distributed run-time targets OPAL-

RT in order to achieve real-time performance. RT-LAB XHP (eXtra High Performance)

mode allows very fast computation of the real-time model on the target OPAL-RT [25].

6.10.2 OpComm block

The OpComm block is used in a Console, Master, or Slave subsystem in the real-time

model for simulation as a real time communication link shown in figure 6.21and 6.22.

Any signal in the real-time model must need to use this OpComm block before entering

a console, Master, or Slave systems. This block provides RT-Lab (RT-Lab system is


Figure 6.20: RT-LAB

in different Operating system and not the same workstation where the model is build)

information about the size and type of data coming from other subsystems.

Figure 6.21: OpComm mask wheninserting inside a SC subsystem

Figure 6.22: OpComm mask wheninserting inside a SM or SS subsystem

Chapter 7

Results

This chapter presents the results from the algorithm after interfacing the electrical refer-

ence network RBTS with DTIA . The distribution of incidence certainty obtained from

correlation of fingerprint pairs. The distribution of incidence certainty are presented in

different fingerprint window size i.e 0.5s,1s,1.5s and 2s from 50Hz and 25Hz measurement

sampling of fingerprint pairs. First a illustrative distribution of incidence certainty are

explained and afterwords both steady state and dynamic behavior of loads result will

be presented .

7.1 Illustrative distribution of incidence certainty

Figure 7.1 shows the illustrative statistical distribution of incidence certainty. The dis-

tribution of the incidence certainties are plotted in each fingerprint window. Correlation

result distribution for the connected bays are in the upper fingerprint window and for

the unconnected bays are in the lower fingerprint window. There is a empty or thresh-

old region shows there is gap between incidence certainty distribution of the connected

bays and the unconnected bays. The blue line is the minimum connected bays cer-

tainty through the bottom outliers of fingerprints window. The red line is the maximum

unconnected bays certainty through the top outliers of fingerprints window. Here the

minimum connected bays certainty shows above the empty region which falls in the con-

nected bays incidence certainty distribution region. The maximum unconnected bays

certainty shows bellow the empty region which falls in the unconnected bays incidence

certainty distribution region. So, there is a separation between connected and uncon-

nected incidence certainty distribution in logarithmic scale −10−4 to −10−6 as shown in

figure .

48

Chapter 7. Validation 49

Figure 7.1: Illustrative distribution of incidence certainty

7.2 Incidence certainty distribution for static loads and

generators

Figure 7.2 shows the incidence certainty distribution for static loads and generators at

fingerprint window of 0.5s. Sampling of measurements current are taken at 50 Hz. Here

rms currents of bays are steady state. Here is a separation between connected and

unconnected incidence certainty distribution in logarithmic scale approximately −10−7

to −10−8 as shown in figure. Except at 8 seconds the maximum unconnected bays

incidence certainty touches threshold line approximate −10−8. This is a case of static

loads and generators but still one can see that there is a separation between connected

and unconnected incidence certainty distribution. In few point there is a chance to

overlapping between connected and unconnected bays. But this overlapping becomes

disappear in figures 7.3 and 7.4 for the fingerprint window size of 1s and 1.5s respectively.

7.3 Incidence certainty distribution for dynamic loads and

generators

This section presents the results of incidence certainty distribution for dynamic loads

and generators for different fingerprint window size and with 50 Hz and 25 Hz samples

of measurement current.


Figure 7.2: Incidence certainty distribution for static loads and generators at 0.5s

Figure 7.3: Incidence certainty dis-tribution for static loads and genera-

tors at 1s

Figure 7.4: Incidence certainty dis-tribution for static loads and genera-

tors at 1.5s

7.3.1 Incidence certainty distribution with sampling frequency 50 Hz

Figure 7.5 shows the incidence certainty distribution at a fingerprint window of 1s with

sampling frequency 50 Hz. Here the threshold region is in between −10−7 to −10−8.

The maximum incidence certainty for the unconnected bay and the minimum incidence

certainty for the connected bay is at 8.5s but still there is a separation by the threshold

region. Figure 7.6 and 7.7 shows that the distribution of incidence certainty with finger

print window size 1.5s and 2s has clearer threshold region than 1s.

7.3.2 Incidence certainty distribution with sampling frequency 25 Hz

Figure 7.8 shows the incidence certainty distribution at a fingerprint window of 1s with

sampling frequency 25 Hz . This case incidence certainty distribution plots from 25

samples in each seconds instead of 50 samples. Here also threshold region is in −10−7 to

−10−8 range and there is a separation between connected and unconnected bays. Figure


Figure 7.5: Incidence certainty distribution at a fingerprint window of 1s with sam-pling frequency 50 Hz

Figure 7.6: Incidence certainty dis-tribution at a fingerprint window of

1.5s with sampling frequency 50 Hz

Figure 7.7: Incidence certainty dis-tribution at a fingerprint window of 2s

with sampling frequency 50 Hz

7.9 and 7.10 shows the incidence certainty distribution at a fingerprint window of 1.5s

and 2s with sampling frequency 25 Hz respectively.

7.4 Discussion

From the above all figures it can be seen that the distribution of incidence certainty for

connected bays and unconnected bays are separated by threshold region, now presented

in the table bellow.

• From the table 7.1 it can be seen that the threshold region is wider for the stochas-

tic loads and generators than the threshold region for static loads and generators.


Figure 7.8: Incidence certainty distribution at a fingerprint window of 1s with sam-pling frequency 25 Hz

Figure 7.9: Incidence certainty dis-tribution at a fingerprint window of

1.5s with sampling frequency 25 Hz

Figure 7.10: Incidence certainty dis-tribution at a fingerprint window of 2s

with sampling frequency 25 Hz


Table 7.1: Incidence certainty distribution for different sampling frequency and fin-gerprint window size

Static/Stochastic Figure Threshold region Comments

Static 50Hz @0.5s 7.2 −10−7 to −10−8 No overlappingStatic 50Hz @1s 7.3 −10−7 to −10−8 No overlapping, more clearStatic 50 Hz @1.5s 7.4 −10−7 to −10−8 No overlapping, more clearStochastic 50Hz @1s 7.5 −10−6 to −10−8 No overlappingStochastic 50Hz @1.5s 7.6 −10−6 to −10−8 No overlapping, more clearStochastic 50Hz @2s 7.7 −10−6 to −10−8 No overlapping, more clearStochastic 25Hz @1s 7.8 −10−6 to −10−8 No overlappingStochastic 25Hz @1.5s 7.9 −10−6 to −10−8 No overlapping, more clearStochastic 25Hz @2s 7.10 −10−6 to −10−8 No overlapping, more clear

Figures 7.5, 7.6 and 7.7 are with stochastic loads and generators shows more en-

tropy into the system which is more likely to occur compare to static loads and

generators which is less likely to occur. For stochastic case we found promising

behavior from the algorithm .

• It is easy to distinguished between connected and unconnected bays for larger

finger print size.

• Both 50 Hz and 25Hz sampling frequency results are quite similar with very neg-

ligible difference.

• For steady state case incidence certainty distribution for connected and uncon-

nected bays threshold region is smaller than stochastic case. A heuristic method

can be used to detect and filter out uncharacteristic changes in incidence certainties

but would require additional functionality that would be difficult to test. However

steady state loads and generators is less likely situation for the real time power

system.

Chapter 8

Conclusion

8.1 Conclusion

This master thesis project had built an IEEE Bus 4 reference electrical distribution

network in Simulink platform to analyze the performance of the algorithm DTIA.

Validation of the simulation model has performed through load flow analysis , steady

stated current and voltage analysis for an accurate electrical distribution network. Af-

ter interfacing the reference network with real-time simulator we found difficulties to

access process measurements through OPC-server. Due to difficulties with support and

complex development in the real time interfacing and there was a high risk to get an

analysis done by the thesis time frame. So the validation of the algorithm has performed

interfacing with off-line Simulink reference network.

One important conclusion is that after interfacing the electrical reference network in

off-line with algorithm to infer the topology of the network , the incidence certainty

distribution for dynamic behavior of load case has found promising behavior from the

algorithm .

We use steady state loads and generators in the reference network to analyze and found

incidence certainty distribution threshold region is smaller compare to stochastic loads

and generators. However steady state loads and generators is less likely situation for the

real time power system .

From the results of the present study we can conclude it is feasible to infer the topology

of the electric power system network from the time stamped process measurements of

currents from bays . This is a related work of the proposed application [3] for topology

implementation of electrical distribution network by using DTIA and further research

should carried out to establish this application for industrial implementation .

54

Chapter 9

Recommendations for future work

9.1 Recommendations for future work

In order to use DTIA application for the distribution network several further analysis

should be made. Examples of such further analysis are presented bellow

• To obtain a real-time performance analysis of DTIA algorithm should be tested

with the reference network with different case study i.e. topology implementation

during fault in the network, circuit breaker(on/off) analysis etc. This is the most

interesting performance analysis of DTIA algorithm for industrial relevant and

demonstration purpose. The validation of DTIA will be much more acceptable

from Industrial prospects to accept this application.

• Analysis with Hardware(IED’s) interfacing with DTIA. As both steady state and

dynamic analysis of DTIA algorithm shows promising performance it is assumed

that it will continue to show similar performance in the case of real time analysis as

well. Therefor the next interesting analysis is to see both performance of collecting

process measurements from the IED for the DTIA by using IEC 61850 standard of

SAS. Here the key part of the work is to identify each substations local structure.

Afterwards by interfacing with IED one can see how these distributed application

performs to communicate each other to determine the topology.

• Finally field test on real distribution network. If the previous two analysis shows

positive performance and sounds realistic then the application can be tested with

real electrical network.

55

Appendix A

XML Trees for connected bays

<?xml version ="1.0"? >

<connections >

<connection number ="1">

<bays >

<bay name=" SP1_F1_1"/>

<bay name=" F1_D1_1"/>

</bays >

</connection >


<bays >



</bays >

</connection >


<bays >



</bays >

</connection >


<bays >



</bays >

</connection >


<bays >

<bay name=" F1_D5_10"/>

56

Appendix B. XML Trees for connected bays 57

<bay name=" F1_D4_10"/>

</bays >

</connection >


<bays >

<bay name=" F2_D1_15"/>

<bay name=" F2_D2_15"/>

</bays >

</connection >


<bays >

<bay name=" F2_D2_17"/>

<bay name=" F2_D3_17"/>

</bays >

</connection >


<bays >

<bay name=" SP1_F3_19 "/>

<bay name=" F3_D1_19"/>

</bays >

</connection >


<bays >

<bay name=" F3_D1_21"/>

<bay name=" F3_D2_21"/>

</bays >

</connection >


<bays >

<bay name=" F3_D2_23"/>

<bay name=" F3_D3_23"/>

</bays >

</connection >


<bays >

<bay name=" F3_D3_26"/>

<bay name=" F3_D4_26"/>

</bays >

</connection >


<bays >


<bay name=" F3_D4_28"/>

<bay name=" F3_D5_28"/>

</bays >

</connection >


<bays >


<bay name=" F4_D1_31"/>

</bays >

</connection >


<bays >

<bay name=" F4_D1_33"/>

<bay name=" F4_D2_33"/>

</bays >

</connection >


<bays >

<bay name=" F4_D3_39"/>

<bay name=" F4_D4_39"/>

</bays >

</connection >


<bays >

<bay name=" F4_D4_41"/>

<bay name=" F4_D5_41"/>

</bays >

</connection >


<bays >

<bay name=" F5_D1_44"/>


</bays >

</connection >


<bays >

<bay name=" F5_D1_46"/>

<bay name=" F5_D2_46"/>

</bays >


</connection >


<bays >

<bay name=" F5_D2_48"/>

<bay name=" F5_D3_48"/>

</bays >

</connection >


<bays >


<bay name=" F6_D1_50"/>

</bays >

</connection >


<bays >

<bay name=" F6_D1_52"/>

<bay name=" F6_D2_52"/>

</bays >

</connection >


<bays >

<bay name=" F6_D2_54"/>

<bay name=" F6_D3_54"/>

</bays >

</connection >


<bays >


<bay name=" F7_D1_56"/>

</bays >

</connection >


<bays >

<bay name=" F7_D3_63"/>

<bay name=" F7_D4_63"/>

</bays >

</connection >


<bays >


<bay name=" F7_D4_65"/>

<bay name=" F7_D5_65"/>

</bays >

</connection >


<bays >

<bay name=" F7_D2_60"/>

<bay name=" F7_D3_60"/>

</bays >

</connection >


<bays >

<bay name=" F4_D2_36"/>

<bay name=" F4_D3_36"/>

</bays >

</connection >


<bays >

<bay name=" F7_D1_58"/>

<bay name=" F7_D2_58"/>

</bays >

</connection >


<bays >

<bay name=" F7_D1_56"/>

<bay name=" SP3_F7_dn "/>

</bays >

</connection >

<connection number ="501" >

<bays >

<bay name=" F6_D1_50"/>

<bay name=" SP3_F6_dn "/>

</bays >

</connection >


<bays >

<bay name=" F1_D5_DG1 "/>

<bay name=" F7_D5_DG1 "/>

</bays >


</connection >


<bays >

<bay name=" F5_D1_44"/>

<bay name=" SP2_F5_Dn "/>

</bays >

</connection >

</connections >

Appendix B

Load flow Table for RBTS

62

Appendix A. Load flow Table for RBTS 63

Brnch

#

From

Bus

To

Bus

From Bus

P (MW)

Injection

Q (MVAr)

To Bus

P(MW)

Injection

Q (MVAr)

Loss

P(MW)

Loss

Q(MVAr)

1 1 2 6.26 0.76 -6.14 -0.49 0.119 0.27

2 2 3 0.89 0.00 -0.89 -0.00 0.002 0.00

3 2 4 5.25 0.48 -5.16 -0.28 0.093 0.21

4 4 5 0.89 0.01 -0.89 -0.00 0.003 0.01

5 4 6 4.27 0.27 -4.20 -0.13 0.064 0.14

6 6 7 0.89 0.00 -0.89 -0.00 0.002 0.00

7 6 8 3.31 0.12 -3.28 -0.04 0.037 0.08

8 8 9 0.89 0.01 -0.89 -0.00 0.003 0.01

9 8 10 0.82 0.01 -0.81 -0.00 0.002 0.01

10 8 11 1.57 0.03 -1.56 -0.01 0.007 0.02

11 11 12 0.67 0.00 -0.67 -0.00 0.002 0.00

12 11 13 0.89 0.01 -0.89 -0.00 0.003 0.01

13 1 15 5.92 0.48 -5.81 -0.23 0.113 0.25

14 15 14 1.64 0.02 -1.63 0.00 0.007 0.02

15 15 16 4.17 0.21 -4.11 -0.08 0.058 0.13

16 16 17 2.46 0.04 -2.45 -0.00 0.020 0.04

17 16 18 1.65 0.04 -1.64 -0.02 0.007 0.02

18 18 19 1.64 0.02 -1.63 -0.00 0.009 0.02

19 1 21 5.89 0.57 -5.78 -0.34 0.105 0.24

20 21 20 0.89 0.01 -0.89 0.00 0.003 0.01

21 21 22 4.89 0.33 -4.83 -0.20 0.060 0.14

22 22 23 0.89 0.01 -0.89 0.00 0.003 0.01

23 22 24 3.94 0.19 -3.89 -0.07 0.053 0.12

24 24 25 0.89 0.01 -0.89 0.00 0.003 0.01

25 24 26 0.82 0.00 -0.81 -0.00 0.002 0.00

26 24 27 2.18 0.06 -2.17 -0.02 0.017 0.04

27 27 28 0.82 0.01 -0.81 -0.00 0.002 0.01

28 27 29 1.35 0.02 -1.35 -0.01 0.005 0.01

29 29 31 0.67 0.00 -0.67 -0.00 0.002 0.00

30 29 30 0.67 0.00 -0.67 0.00 0.001 0.00

31 1 33 6.88 0.82 -6.73 -0.47 0.154 0.35

32 33 32 0.89 0.01 -0.89 -0.00 0.003 0.01

33 33 34 5.84 0.47 -5.72 -0.21 0.116 0.26

34 34 35 0.89 0.00 -0.89 -0.00 0.002 0.00

35 34 36 0.89 0.01 -0.89 -0.00 0.003 0.01

36 34 37 3.94 0.20 -3.89 -0.07 0.055 0.12

37 37 38 0.89 0.01 -0.89 -0.00 0.003 0.01

38 37 39 0.82 0.00 -0.81 -0.00 0.002 0.00

39 37 40 2.18 0.06 -2.17 -0.02 0.017 0.04

40 40 41 0.82 0.01 -0.81 -0.00 0.002 0.01

41 40 42 1.35 0.02 -1.35 -0.01 0.005 0.01

42 42 43 0.67 0.00 -0.67 -0.00 0.002 0.00

43 42 44 0.67 0.00 -0.67 -0.00 0.001 0.00

44 1 46 5.03 0.32 -4.95 -0.14 0.081 0.18

45 46 45 1.64 0.02 -1.63 0.00 0.008 0.02

Appendix A. Load flow Table for RBTS 64

Brnch

#

From

Bus

To

Bus

From Bus

P (MW)

Injection

Q (MVAr)

To Bus

P(MW)

Injection

Q (MVAr)

Loss

P(MW)

Loss

Q(MVAr)

46 46 47 3.31 0.12 -3.28 -0.06 0.027 0.06

47 47 48 1.64 0.02 -1.63 0.00 0.009 0.02

48 47 49 1.65 0.03 -1.64 -0.02 0.009 0.02

49 49 50 1.64 0.02 -1.63 -0.00 0.007 0.02

50 1 52 5.92 0.49 -5.82 -0.25 0.106 0.24

51 52 51 1.64 0.02 -1.63 0.00 0.007 0.02

52 52 53 4.18 0.23 -4.12 -0.10 0.058 0.13

53 53 54 1.64 0.02 -1.63 -0.00 0.009 0.02

54 53 55 2.48 0.08 -2.46 -0.04 0.021 0.05

55 55 56 2.46 0.04 -2.44 -0.00 0.016 0.04

56 1 58 6.12 0.62 -6.01 -0.37 0.114 0.26

57 58 57 0.89 0.01 -0.89 0.00 0.003 0.01

58 58 59 5.12 0.36 -5.05 -0.21 0.066 0.15

59 59 60 0.89 0.01 -0.89 0.00 0.003 0.01

60 59 61 4.16 0.21 -4.11 -0.08 0.056 0.13

61 61 62 0.89 0.00 -0.89 0.00 0.002 0.00

62 61 63 0.89 0.01 -0.89 0.00 0.003 0.01

63 61 64 2.33 0.07 -2.31 -0.03 0.018 0.04

64 64 65 0.82 0.00 -0.81 -0.00 0.002 0.00

65 64 66 1.50 0.03 -1.49 -0.01 0.008 0.02

66 66 67 0.82 0.01 -0.81 -0.00 0.002 0.01

67 66 68 0.67 0.00 -0.67 -0.00 0.001 0.00

Total 1.805 4.06

Ap

pen

dix

A.

Loa

dfl

ow

Table

for

RB

TS

65

Block typeBustype

BusID

Vbase(KV)

Vref(pu)

Vangle(deg)

P(MW)Q

(Mvar)V LF(pu)

VangleLF(pu)

P LF(MW)

Q LF(Mvar)

BlockName

DYN load PQ *1* 0.40 1 0.00 0.81 0.00 0.90 17.84 0.81 0.00 load 5/L5DYN load PQ *2* 0.40 1 0.00 0.67 0.00 0.92 18.86 0.67 0.00 Load 17/L17DYN load PQ *3* 0.40 1 0.00 0.89 0.00 0.92 18.05 0.89 0.00 Load 2/L2DYN load PQ *4* 0.40 1 0.00 0.89 0.00 0.91 17.63 0.89 0.00 Load 3/L3DYN load PQ *5* 0.40 1 0.00 0.81 0.00 0.89 17.13 0.81 0.00 Load 37/L37DYN load PQ *6* 0.40 1 0.00 0.81 0.00 0.90 17.11 0.81 0.00 Load 36/L36DYN load PQ *7* 0.40 1 0.00 0.89 0.00 0.90 16.89 0.89 0.00 Load 34/L34DYN load PQ *9* 0.40 1 0.00 0.89 0.00 0.90 16.86 0.89 0.00 Load 35/L35DYN load PQ *8* 11.00 1 0.00 0.00 0.00 0.92 -10.03 0.00 0.00 WT5/WT4DYN load PQ *10* 0.40 1 0.00 0.89 0.00 0.91 17.13 0.89 0.00 Load 33/L33DYN load PQ *11* 11.00 1 0.00 2.44 0.00 0.93 -9.76 2.44 0.00 Load 31/L31DYN load PQ *12* 11.00 1 0.00 1.36 0.00 0.94 -9.23 1.36 0.00 Load 27/L27DYN load PQ *13* 0.40 1 0.00 0.89 0.00 0.94 18.75 0.89 0.00 Load 18/L18DYN load PQ *14* 0.40 1 0.00 0.89 0.00 0.92 18.11 0.89 -0.00 Load 21/L21DYN load PQ *15* 0.40 1 0.00 0.81 0.00 0.92 18.40 0.81 0.00 Load 22/L22DYN load PQ *16* 0.40 1 0.00 0.67 0.00 0.92 18.88 0.67 0.00 Load 25/L25DYN load PQ *17* 0.40 1 0.00 0.67 0.00 0.92 18.86 0.67 0.00 Load 24/L24DYN load PQ *18* 0.40 1 0.00 0.67 0.00 0.92 18.88 0.67 0.00 Load 16/L16DYN load PQ *35* 11.00 1 0.00 -1.63 0.00 0.94 -8.79 -1.63 -0.00 DG4/DG3DYN load PQ *19* 0.40 1 0.00 0.89 0.00 0.93 18.35 0.89 0.00 Load 13/L13DYN load PQ *20* 0.40 1 0.00 0.81 0.00 0.93 18.65 0.81 -0.00 Load 14/L14Bus - *21* 33.00 1 0.00 0.00 0.00 0.99 -2.08 0.00 0.00 Tx5Bus - *22* 33.00 1 0.00 0.00 0.00 0.99 -2.16 0.00 0.00 Tx4Vsrc swing *23* 33.00 1 0.00 100.00 0.00 1.00 0.00 29.67 6.87 Three-Phase SourceDYN load PQ *24* 0.40 1 0.00 0.89 0.00 0.94 18.62 0.89 0.00 Load 1/L1DYN load PQ *25* 0.40 1 0.00 0.89 0.00 0.90 17.10 0.89 0.00 Load4/L4DYN load PQ *26* 11.00 1 0.00 -1.32 0.00 0.93 -9.28 -1.32 -0.00 WT2/WT1Bus - *30* 11.00 1 0.00 0.00 0.00 0.98 -7.69 0.00 0.00 Tx1DYN load PQ *27* 11.00 1 0.00 1.63 0.00 0.93 -9.73 1.63 0.00 Load 10/L10DYN load PQ *28* 11.00 1 0.00 2.44 0.00 0.93 -9.60 2.44 0.00 Load 9/L9

Ap

pen

dix

A.

Loa

dfl

ow

Table

for

RB

TS

66

Block typeBustype

BusID

Vbase(KV)

Vref(pu)

Vangle(deg)

P(MW)Q

(Mvar)V LF(pu)

VangleLF(pu)

P LF(MW)

Q LF(Mvar)

BlockName

DYN load PQ *29* 11.00 1 0.00 1.63 0.00 0.95 -8.77 1.63 0.00 Load 8/L8DYN load PQ *31* 0.40 1 0.00 0.89 0.00 0.94 18.76 0.89 -0.00 Load 11/L11DYN load PQ *32* 0.40 1 0.00 0.89 0.00 0.93 18.56 0.89 0.00 Load 12/L12DYN load PQ *33* 11.00 1 0.00 -1.35 0.00 0.95 -8.54 -1.35 0.00 WT3/WT4DYN load PQ *34* 0.40 1 0.00 0.81 0.00 0.92 18.44 0.81 0.00 Load 15/L15DYN load PQ *36* 0.40 1 0.00 0.81 0.00 0.92 18.40 0.81 -0.00 Load 23/L23DYN load PQ *37* 11.00 1 0.00 -1.00 0.00 0.94 -8.77 -1.00 0.00 WT1/WT3DYN load PQ *38* 0.40 1 0.00 0.89 0.00 0.93 18.34 0.89 0.00 Load 19/L19DYN load PQ *39* 0.40 1 0.00 0.89 0.00 0.92 18.31 0.89 -0.00 Load 20/L20Bus - *40* 11.00 1 0.00 0.00 0.00 0.98 -7.60 0.00 0.00 Tx5DYN load PQ *41* 11.00 1 0.00 1.63 0.00 0.95 -8.74 1.63 0.00 Load 26/L26DYN load PQ *42* 11.00 1 0.00 1.63 0.00 0.93 -9.67 1.63 -0.00 Load 28/L28DYN load PQ *44* 11.00 1 0.00 -0.89 0.00 0.94 -9.48 -0.89 0.00 DG2/DG2DYN load PQ *44* 11.00 1 0.00 -0.00 0.00 0.94 -9.48 -0.00 0.00 DG44/DG1DYN load PQ *45* 11.00 1 0.00 1.63 0.00 0.93 -9.79 1.63 -0.00 Load 30/L30DYN load PQ *46* 11.00 1 0.00 1.63 0.00 0.94 -9.57 1.63 0.00 Load 29/L29Bus - *47* 11.00 1 0.00 0.00 0.00 0.96 -8.99 0.00 0.00 Tx4DYN load PQ *48* 0.40 1 0.00 0.89 0.00 0.92 17.43 0.89 0.00 Load 32/L32DYN load PQ *49* 0.40 1 0.00 0.67 0.00 0.90 17.64 0.67 0.00 Load 38/L38DYN load PQ *51* 0.40 1 0.00 0.89 0.00 0.89 16.86 0.89 -0.00 Load 7/L7DYN load PQ *50* 11.00 1 0.00 -0.89 0.00 0.91 -10.03 -0.89 0.00 DG3/DG1DYN load PQ *52* 0.40 1 0.00 0.67 0.00 0.90 17.61 0.67 0.00 load 6/L6DYN load PQ *51* 11.00 1 0.00 -1.32 0.00 1.22 0.46 -1.32 0.00 WT5

Appendix C

Machine Initialization

Machine: Load 9/L9 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -10.55 Uab: 9725.4 Vrms

[0.8841 pu] 19.45 Ubc: 9725.4 Vrms [0.8841 pu] -100.55 Uca: 9725.4 Vrms [0.8841 pu] 139.45 Ia: 133.96

Arms -5.22 Ib: 133.96 Arms -125.22 Ic: 133.96 Arms 114.78 P: 2.2468e+006 W Q: -2.0965e+005 Vars

Machine: Load 2/L2 Nominal: 400 V rms Bus Type: P & Q load Uan phase: 19.19 Uab: 335.99 Vrms

[0.84 pu] 49.19 Ubc: 335.99 Vrms [0.84 pu] -70.81 Uca: 335.99 Vrms [0.84 pu] 169.19 Ia: 1346.5 Arms

-4.94 Ib: 1346.5 Arms -124.94 Ic: 1346.5 Arms 115.06 P: 7.1517e+005 W Q: 3.2031e+005 Vars



Arms -6.78 Ib: 1368.1 Arms -126.78 Ic: 1368.1 Arms 113.22 P: 6.9982e+005 W Q: 3.414e+005 Vars

Machine: load 5/L5 Nominal: 400 V rms Bus Type: P & Q load Uan phase: 19.82 Uab: 324.14 Vrms



Machine: Load 10/L10 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -10.74 Uab: 9705.5

Vrms [0.8823 pu] 19.26 Ubc: 9705.5 Vrms [0.8823 pu] -100.74 Uca: 9705.5 Vrms [0.8823 pu] 139.26 Ia:

89.678 Arms -5.47 Ib: 89.678 Arms -125.47 Ic: 89.678 Arms 114.53 P: 1.5012e+006 W Q: -1.3848e+005

Vars


[0.8372 pu] 51.09 Ubc: 334.89 Vrms [0.8372 pu] -68.91 Uca: 334.89 Vrms [0.8372 pu] 171.09 Ia: 1031

Arms -7.26 Ib: 1031 Arms -127.26 Ic: 1031 Arms 112.74 P: 5.2631e+005 W Q: 2.8394e+005 Vars







67

Appendix A. Machine Initialization 68


[0.8544 pu] 49.73 Ubc: 341.75 Vrms [0.8544 pu] -70.27 Uca: 341.75 Vrms [0.8544 pu] 169.73 Ia: 1334

Arms -4.15 Ib: 1334 Arms -124.15 Ic: 1334 Arms 115.85 P: 7.2203e+005 W Q: 3.1964e+005 Vars

Machine: WT2/WT1 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -7.93 Uab: 9555 Vrms

[0.8686 pu] 22.07 Ubc: 9555 Vrms [0.8686 pu] -97.93 Uca: 9555 Vrms [0.8686 pu] 142.07 Ia: 74.981 Arms

174.42 Ib: 74.981 Arms 54.42 Ic: 74.981 Arms -65.58 P: -1.2399e+006 W Q: 50909 Vars

Machine: WT3/WT4 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -7.13 Uab: 9761 Vrms






Machine: Load4/L4 Nominal: 400 V rms Bus Type: P & Q load Uan phase: 19.00 Uab: 320.65 Vrms



Machine: DG2/DG2 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -10.40 Uab: 9779 Vrms






Machine: DG4/DG3 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -6.88 Uab: 9620.6 Vrms


Arms 174.53 Ib: 89.678 Arms 54.53 Ic: 89.678 Arms -65.47 P: -1.4939e+006 W Q: 36818 Vars



171.12 Ib: 0.090689 Arms 51.12 Ic: 0.090689 Arms -68.88 P: -1535.5 W Q: 40.728 Vars















































Vars




Vars





Vars




Vars

























Machine: WT1/WT3 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -6.99 Uab: 9642.4 Vrms


Arms 174.78 Ib: 56.195 Arms 54.78 Ic: 56.195 Arms -65.22 P: -9.3807e+005 W Q: 29111 Vars

Machine: WT5/WT4 Nominal: 11 kV rms Bus Type: P & Q load Uan phase: -8.93 Uab: 9406.7 Vrms

[0.8552 pu] 21.07 Ubc: 9406.7 Vrms [0.8552 pu] -98.93 Uca: 9406.7 Vrms [0.8552 pu] 141.07 Ia: 8.8199e-

005 Arms -7.06 Ib: 8.8199e-005 Arms -127.06 Ic: 8.8199e-005 Arms 112.94 P: 1.4362 W Q: -0.046884

Vars

Machine: load 6/L6 Nominal: 400 V rms Bus Type: P & Q load Uan phase: 19.58 Uab: 322.6 Vrms



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Documents

Validation of Distributed Topology Inference of ...709827/FULLTEXT01.pdf · I want to thanks my friends in KTH ;Shaiyek Taslim Buland , Mainuddin Ahmed Deep,Chaitaniyay Arivind Deshpande