HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS

HAROLD RENÉ CHAMORRO VERA

UNIVERSIDAD DE LOS ANDES

FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA

BOGOTÁ D.C.

2012

HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS

HAROLD RENÉ CHAMORRO VERA

TESIS DE MAESTRÍA PARA OPTAR AL TÍTULO DE

MAGÍSTER EN INGENIERÍA ÁREA: INGENIERÍA ELÉCTRICA

ASESOR

Ing. MARIO ALBERTO RÍOS MESÍAS. PhD

UNIVERSIDAD DE LOS ANDES

FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA

BOGOTÁ D.C.

2012

PROTOTIPO DE REDES INTELIGENTES PARA CONTROL JERÁRQUICO DE

SISTEMAS INTERCONECTADOS

HAROLD RENÉ CHAMORRO VERA

TESIS DE MAESTRÍA PARA OPTAR AL TÍTULO DE

MAGÍSTER EN INGENIERÍA ÁREA: INGENIERÍA ELÉCTRICA

ASESOR

Ing. MARIO ALBERTO RÍOS MESIAS PhD

UNIVERSIDAD DE LOS ANDES

FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA

BOGOTÁ D.C.

2012

HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS

Harold René Chamorro Vera

Universidad de Los Andes

(Abstract)

The technological development and enhancement of new strategies related with

Wide Area Measurement and Control Systems (WAMCS) is playing a key role

with large interconnected power systems in order to assure stability under Low

Frequency Oscillations (LFO). In this thesis a hierarchical intelligent controller

scheme for multi-machine systems is presented with the purpose to maximise the

damping factor of intra-area and inter-area oscillations combining the principles of

Fuzzy Logic Control and aMulti-Agent Systems (MAS) architecture. The controller

designing takes advantage of the controllability and observability definitions to

obtain the optimal location of the local Power System Stabilizers (PSS) and the

Phasor Measurement Units (PMU) where the local PSS has been modelled with

fuzzy logic as well. An algorithm to measure the damping of any signal is

developed and used as PMU measurement in the tie-lines. The time-domain

response of the designed controller is tested in a power benchmark system

demonstrating its adaptability and performance.

To my mom

Research is to see what everybody else has seen, and to think what nobody else has thought.

~Albert Szent-Gyorgyi

ACKNOWLEDGEMENTS

First of all and the most important thing is that I am deeply indebted to my

supervisor Prof. Mario Ríos for his support, motivation and inspiring discussions

throughout the years of my studies. He always had suggestions, answered my

numerous questions, and put forth the effort to help me progress. I benefited a lot

from his great research experience, technical advices and all courses he taught.

I am grateful to Dr. Mauricio Guerrero for helping me in my initial phase and for

getting me started in working on powersystems in the university.

It has been an honour to be associated with the Department of Electrical and

Electronics Engineering at Universidad de los Andes.

I would also like to extend my gratitude to Dr.Gustavo Ramos for his continuous

help and support. I believe that I have been truly blissful by working with him

over the past two years. I have benefited not only from his knowledge in power

quality area, but also from his keen personality. I wish him well in all his future

endeavours.

I would also like to thank my colleagues, for their valuable comments and

assistance, and wish them all the best of luck and brightest future. Special thanks

to Mr.CamiloOrdoñez who was not just a colleague but a brother who I will

always remember. His deep insights in the field of power systems and our long

and creative discussions have made this collaboration particularly fruitful. Without

him, many results couldn’t have been obtained and the part on power systems

would be significantly shorter and weaker.

Thanks and appreciation is given to Dr. Ricardo Moreno whose advice and

encouragement was always of great help. He helped me significantly with all my

questions about practically everything related to power systems and optimization.

Indeed, I have discussed most of my ideas first with him due to his invaluable

feedback.

Thanks go also to Dr. Oscar “Simon Dice” Gomez, who is not only the group’s

PSAT guru, but also for having time to answer my numerous questions about

power systems in general towards the study case.

I would like to send special thanks to Ms. Gloria Martinez, for helping me with any

administrative procedure or power system stability questions I had. She was more

than supportive all of the time.

I have been fortunate to come across many funny and good friends, without whom

life would be bleak and I cannot forget, – in particular Mr. Alfredo “Degenerati”

Tobón, Mr. Juan Alberto “Pulu”Ramirez, Mr.Juan David “Poli” Beltran, Mr.Juan

Carlos “Totuma” Díaz, Mr. Nelson “Gala” Barreraand Mr.JulioMonroy.

I would like to express my deepest appreciation to Ms. Manuela Medina, Ms.

Alejandra “LiaLios” Fajardo and Ms.Dayana Herrera. Their endless love, support

and encouragement were emotionally critical, without which my pursuit of a

master’s degree would not have been possible.

I present my appreciation to all of my professors and teachers from the department

and those from all other segments of life: Prof.Fernando Jimenez, Prof.Angela

Cadena, Prof. Alvaro Torres, Prof. NicanorQuijano and Prof. Alain Gauthier for

their help, discussions and advice.

For this research, some details are essential. Many people helped with this, for

which I would like to thank them whole heartedly, special thanks to Mr. Andres

“Iguazo” Leal and Ms. Ana MaríaOspina. Without their generosity there would be

nothing to work with.

I would also like to acknowledge Mr.Efren Martinez, Mr. Elkin “El-king” Cantor,

Mr. Diego “Fosforito” Salamanca, Mr. Jose Daza, Mr. Jaime Osorio, Mr. Cesar

Rodríguez, Mr. Daniel Blandón and Mr. Victor Melo for taking over the main work

load on power systems and collaborating with me on this topic.

I also express my gratitude to everyone in the IEEE Uniandes Student Branch.

These include, just tomention a few: Mr. Juan Sebastian Moya, Mr. Carlos

Quintero, Ms. Jessica Buritica, Mr. Jorge MarioGarzon, Ms. Diana Pardo, Ms. Paula

Florez, Mr. Gabriel Sanchez, Mr. Diego Campo and Mr. Nicolas Velasquez.

Looking back, the productive phase of my master’s degree was triggered by Mr.

Carlos Macana and Ms. Liz Catherine Hernandez, who suggested me to study and

introduced me to the field of Power Systems, affected and inspired me with their

creativity, taught me many things, and showed me how to focus on the important

aspects of research. I am deeply grateful for that.

Furthermore, I am grateful to collaborate and work with Mr. Andres Ovalle, Mr.

Andres Puentes and Mr. Jose Calderónachieving excellent results that got later

awarded and published.

I am also glad to have had the opportunity to work with the Power and Energy

Group in the University and to know their members who provided a great and

pleasant atmosphere and helped me achieve my goal.

Last but not least, I would also like to thank my mom for her never-ending

support, love, encouragement and prayers that helped me complete this work. She

gave me the opportunity to be here and pursue this degree and reminded me that

there is a life beyond the university. Nothing I can say, can thank her enough.

I would like to acknowledge that there is a greater power than me that made all of

this possible. Thanks God for all his wonderful blessings, without Him, none of

what I haveachieved would exist.

1

TABLE OF CONTENTS

I. INTRODUCTION .................................................................................................................... 7

II. OBJECTIVES ......................................................................................................................... 11

4.1 General Objective ........................................................................................................ 11

4.2 Specific Objectives ....................................................................................................... 11

1. SMART TRANSMISSION GRIDS ............................................................................................ 12

1.1 Background .................................................................................................................. 12

1.2 Intelligence Requirement ............................................................................................ 13

1.3 Multi- Agents in Smart Transmission Grids ................................................................. 14

1.4 Thesis Statement ......................................................................................................... 15

1.6 Organisation of the thesis ............................................................................................ 16

2. PMU AND PSS LOCATION .................................................................................................... 17

2.1 Power System Proposed .............................................................................................. 18

2.2 Eigenvalue Analysis ...................................................................................................... 19

2.3 Participation Factors .................................................................................................... 20

2.4 Mode Shapes ............................................................................................................... 20

2.5 Controllability Analysis ................................................................................................ 21

2.6 Voltage Stability Analysis ............................................................................................. 23

3. ON-LINE MEASUREMENTS .................................................................................................. 26

3.1 Damping Algorithm Proposed ..................................................................................... 27

3.2 Relative Amplitude and Frequency Measurements .................................................... 30

3.3 Signal Dynamic Deviation ............................................................................................ 31

3.4 Algorithms Tests .......................................................................................................... 32

3.4.1 Damping Factor Measurement .................................................................................... 32

3.4.2 Frequency Measurement ............................................................................................ 33

3.4.3 Relative Amplitude Measurement ............................................................................... 34

4 MULTI-AGENT SYSTEM DESIGN ........................................................................................... 36

4.1 Application Design ....................................................................................................... 37

4.1.1 Knowledge Modelling .................................................................................................. 37

4.1.1.1 Local Control Model ..................................................................................................... 38

4.1.1.2 Supervisor Control Model ............................................................................................ 40

4.1.2 Tasks Roles ................................................................................................................... 43

4.1.2.1 User Access Task .......................................................................................................... 44

2

4.1.2.2 Control task .................................................................................................................. 44

4.1.2.3 Measurement Task ...................................................................................................... 44

4.1.2.4 Coordination Task ........................................................................................................ 44

4.1.2.5 Bulletin Board Tasks ..................................................................................................... 46

4.1.3 Agent Specification ...................................................................................................... 46

4.1.3.1 Control Agent ............................................................................................................... 46

4.1.3.2 User Agent ................................................................................................................... 47

4.1.3.3 Monitor Agent ............................................................................................................. 47

4.2 Constraints ................................................................................................................... 47

5. MULTIAGENT SYSTEM APPLICATION AND STUDY CASE ..................................................... 48

5.1 Transient Stability ........................................................................................................ 49

5.2 Results and Discussion ................................................................................................. 53

6. CONCLUSIONS ..................................................................................................................... 55

7. FUTURE WORK .................................................................................................................... 56

8. REFERENCES ........................................................................................................................ 56

3

LIST OF FIGURES

Fig. 1 Derived Power System [42] ............................................................................. 18

Fig. 2 Power System Proposed ................................................................................... 19

Fig. 3 Mode Shape Mode 4 ......................................................................................... 21

Fig. 4 Mode Shape Mode 5 ......................................................................................... 21

Fig. 5 Controllability Mode 4 ..................................................................................... 22

Fig. 6 Controllability Mode 5 ..................................................................................... 22

Fig. 7 PSS and PMU Location .................................................................................... 23

Fig. 8 Voltage Stability Analysis ................................................................................ 23

Fig. 9 Stability Plane .................................................................................................... 24

Fig. 10 Damping Algorithm ....................................................................................... 27

Fig. 11 Damping Algorithm Embedded ................................................................... 28

Fig. 12 Damping and Relative Amplitude Measurement Block ........................... 29

Fig. 13 Damping Measurement ................................................................................. 29

Fig. 14 Frequency Measurement Simulink Diagram .............................................. 30

Fig. 15 Frequency Measurement Block ..................................................................... 31

Fig. 16 Signal Deviation Implementation ................................................................. 31

Fig. 17 Damping Measurement between areas 1 and 2.......................................... 32

Fig. 18 Damping Measurement between areas 1 and 3.......................................... 33

Fig. 19 Frequency Measurement between areas 1 and 2 ........................................ 33

Fig. 20 Frequency Measurement between areas 1 and 3 ........................................ 34

Fig. 21 Relative Amplitude Measurement between areas 1 and 2 ....................... 34

Fig. 22 Relative Amplitude Measurement between areas 1 and 3 ....................... 35

Fig. 23 Hierarchical Controller Concept ................................................................... 36

Fig. 24 Proposed Controller Scheme ......................................................................... 37

Fig. 25 Speed deviation and Power Active deviation Input Memberships

functions representation ............................................................................................. 39

Fig. 26 Output Membership Functions ..................................................................... 39

Fig. 27 Takagi Sugeno Control Proposed ................................................................. 41

Fig. 28 Input Membership Functions of the Hierarchical Controller ................... 41

4

Fig. 29 Input Membership Functions of the Hierarchical Controller (Deviation)

........................................................................................................................................ 42

Fig. 30 Surface Control Associated ............................................................................ 42

Fig. 32 Multi – Agent Framework ............................................................................. 48

Fig. 33 Simpower System Implementation .............................................................. 49

Fig. 34 Electrical Power Time Response of Generators (Local) ............................. 50

Fig. 35 Electrical Power Time Response of a Tie-line (Remote) ............................ 50

Fig. 36 Electrical Time Response in the Tie-lines with LC (Local Controllers) and

HC (hierarchical controllers) ...................................................................................... 51

Fig. 37 Electrical Time Response in the Tie-lines with LC (Local Controllers) and

HC (hierarchical controllers) ...................................................................................... 51

Fig. 38 Electrical Power Time Response of Generators with conventional PSS as

long as it is applied the Hierarchical Control .......................................................... 52

Fig. 39 Electrical Power Time response of Generators with the two fuzzy

controllers ..................................................................................................................... 52

5

LIST OF TABLES

Table. I System Eigenvalues ....................................................................................... 19

Table. II Participation Factors .................................................................................... 20

Table. III Eigenvalue Analysis in Buses ................................................................... 24

Table. IV Decision Rules ............................................................................................. 40

Table. V Decision Rules .............................................................................................. 43

Table. VI Controller Comparison .............................................................................. 53

6

GLOSSARY

AI: Artificial Intelligence

FACTS: Flexible AC Transmission Systems

FLC: Fuzzy Logic Control

LFO: Low Frequency Oscillations

MAS: Multi-Agent Systems

PMU: Phasor Measurement Unit

PSS: Power System Stabilizer

RFS: Remote Feedback Signals

SMT: Synchronised Measurement Technology

STG: Smart Transmission Grid

TSO: Transmission System Operator

WAMS: Wide Area Measurement Systems

WACS: Wide Area Control System

WAMCS: Wide Area Measurement and Control System

7

I. INTRODUCTION

As a result to the migration to the new power systems concept known as

“Smart Grid” and conceived as the intelligent automation of electrical

transmission and distribution networks, many different initiatives have been

proposed, especially in small-signal stability area with the purpose to damp the

electromechanical oscillations to assure stable operation of interconnected

systems[1]-[3].

With the development of Wide Area Measurements and Control Systems

(WAMCS) and the technological improvements in the past three decades, the

use of Phasor Measure-ment Units (PMU) has become a reality [4][5], providing

of on-line measurements and bringing the opportunity to design real-time

controllers and algorithms with more accuracy and precision to get the

correspondent supervision of any power system, so that as a general rule, the

Smart Grid systems require the application of intelligent control systems in

order to face and solve the imminent problems related and to give some

autonomous decisions.

Intelligent control based on Artificial Intelligence (AI) and soft computing

techniques have played an important part in different systems solving several

problems in engineering. Fuzzy logic as a method of AI has been applied in

many electrical systems control-related with success [6][7][8].

The use of fuzzy logic in power systems is based on its inherent advantages like

its tolerance with imprecise data, its flexibility and adaptability and the

behavioural abstraction model of large systems capacity without mathematical

complex equations.

8

Concerning to Smart Grids, the applicability of Fuzzy Logic Control (FLC) has

been extended to multiple applications such as fault management [9], self-

healing and diagnose [10], load forecasting [11], and reconfiguration or

restoration [12]. Therefore, there is a high interaction between fuzzy systems

and Smart Grid systems, in which is required some kind of autonomous

decisions under disturbances or operation conditions.

In the framework of the Smart Transmission Grids it has contemplated some

control challenges, which refers to the control centres, their methodology and

the intelligence that can be provided to them and the smart measurements

involved [13].

One of the main problems in transmission levels concerns to the power

fluctuations of small magnitude and Low Frequency Oscillations (LOF) which

can limit the amount of power able to transfer [14], and producing instability

that can provoke outages or several damages along the interconnected systems.

The problem of LOF presented in large interconnected systems concerns in

general to different operative regions and involves different Transmission

System Operators (TSO). A disturbance event should be monitored by different

TSO supported by a communication infrastructure established [15], however, at

the moment the TSO are operated almost uncoordinated based on a Central-

TSO with the logistic actions to solve.

In order to mitigate these oscillations, the Power Systems Stabilizers were

developed as supplementary device that manipulate the injected excitation to

the synchronous machine [16][17].

9

Even though the conventional PSS has been tested and shown the attenuation

of the undesired LOF with good results, some improvements are necessary to

be done due the load variability and the different operation conditions [18]. In

that sense, different works related with PSS improvements using FLC have

been proposed with the objective to do some enhancements as diverse as self-

tunning[19], self-learning [20], comparing different control techniques [21] or

even comparing other defuzzification methods [22].

Consequently, many supervisory and hierarchical control architectures have

been presented in order to increase the performance of conventional PSS under

severe disturbances or high oscillatory systems [23] or coordinate them with

Flexible AC Transmission Systems (FACTS) in large systems where are required

[24].

Some new studies in supervisory-hierarchical controllers based FLC have been

proposed [25][26], two of them include PMU to measure remote signals [1], [27].

In addition, some current perspectives of WAMCS have shown the requirement

of coordinated layers or decision systems with different objectives and process

priorities, executing actions in the local controllers or process therein, which

implies changes in set-points or settings [28].

Along these lines, Muli-agent systems (MAS) have provided of cooperative,

coordination and communication features in different applications in power

systems [29] -[30], nevertheless these kinds of architectures have not been

investigated at all and require more research.

10

This document appears with the purpose to contribute to the evolution and

smartness initiative of power systems, especially in transmission systems,

proposing a Hierarchical Multi-Agent System based Fuzzy Logic Control

(HMASFLC) to increase the damping in the tie-lines and reducing the LOF at

minimum, involving Remote Feedback Signals (RFS), on-line measurements

and, local fuzzy controllers and measurements as well.

11

II. OBJECTIVES

4.1 General Objective

To develop an appropriate hierarchical control methodology for the use in

smart grids with the purpose to assure the power system integrity associated

with the interconnections.

4.2 Specific Objectives

To establish and characterise a suitable power system for the action

control execution.

To plan the required action control according to the contingency and

stability analysis.

To study and compare different control strategies (classical, modern or

intelligent) that can be applied to the studiedpowersystem.

To determine a control technique and evaluate its performance in the

system before different goals.

To emulate the monitoring by PMU (Phasor Measurement Unit) and the

WAMCS concept to analyse whether the controller decisions area

appropriate.

12

1. SMART TRANSMISSION GRIDS

1.1 Background

Different energy programs related with Smart Transmission Grids (STG) are

being developed around the world, some of them are briefly mentioned and

summarised as follows.

The IntelliGrid program, initiated by the Electric Power Research Institution

(EPRI), is to create the technical foundation for a smart power grid that links

electricity with communications and computer control to achieve tremendous

gains in the enhancements of reliability, capacity, and customer service [31][32].

This program provides methodologies, tools, and recommendations for open

standards and requirement-based technologies with the implementation of

advanced metering, distribution automation, demand response, and wide-area

measurement. The interoperability is expected to be enabled between advanced

technologies and the power system.

The SmartGrids program, formed by the European Technology Platform (ETP)

in 2005, created a joint vision for the European networks of 2020 and beyond

[33][34]. Its objective features were identified for Europe’s electricity networks

as flexible to customers’ requests, accessible to network users and renewable

power sources, reliable for security and quality of power supply, and economic

to provide the best value and efficient energy management.

A Federal Smart Grid Task Force was established by the U.S. Department of

Energy (DoE) under Title XIII of the Energy Independence and Security Act of

2007. In its 2030 Grid vision, the objectives are to construct a 21st-century

13

electric system to provide abundant, affordable, clean, efficient, and reliable

electric power anytime, anywhere [35]. The expected achievements, through

smart grid development, will not merely enhance the reliability, efficiency, and

security of the nation’s electric grid, but also contribute to the strategic goalof

reducing carbon emissions.

Remarkable research and development activities are also ongoing in both

industry and academia. References [36] and [37] present smart grids for future

power delivery. Reference [38] discusses the integration issue in the smart grid.

Specific technologies, such as smart metering infrastructure, are presented in

[39].

1.2 Intelligence Requirement

As a general requirement to improve the current transmission grid to the new

“Smart Grid” concept some developments are necessary to do in order to

achieve that smartness.

Intelligent technologies and human expertise will be incorporated and

embedded in the smart transmission grid. Self-awareness of the system

operation state will be available with the aidof online time-domain analysis

such as voltage/angular stabilityand security analysis. Self-healing will be

achieved to enhancethe security of transmission grid via coordinated protection

andcontrol schemes.

Smart sensing and measurementand advanced instrumentation technologies

will serveas the basis for communications, computing, control, andintelligence.

14

Intelligent technologies willenable fuzzy logic reasoning, knowledge discovery,

andself-learning, which are important ingredients integratedin the

implementation of the above advanced technologiesto build a smarter

transmission grid[13].

In the future controlcentre, the system-level information will be obtained

fromthe state measurement modules based on phasor measurementunits

(PMUs) [40],[41]. The PMU-based state measurement isexpected to be more

efficient than the present state estimationsince synchronized phasor signals

provide the state variables,in particular, voltage angles.

1.3 Multi- Agents in Smart Transmission Grids

The intelligent agents at transmission network devices or substations may

interact with neighbour agents to achieve broader information in order to make

improved decisions without extensive communication back to the control

center. In short, the actual control action will be a combination of local decisions

from the distributed intelligent agents, central decisions from the smart control

center, and the “regional” decision based on the information exchange among

peer substations and network devices. Each type of action shall have a different

response time and it’s the most efficient for a particular type of work. The actual

control process may require a few iterations among the three types of actions.

The objective is to contribute to the development of future monitoring, state

estimation and control applications based on synchronized phasor

measurements to improve power system security and increase utilization of the

transmission grid. The main focus will be on dynamic phenomena like voltage

15

stability and the damping of power oscillations over wide geographical areas,

where to locate sensors to improve the situational awareness (e.g. power

oscillations) as well as design and placement of actuators for improved

performance (e.g. stabilizing the oscillations)[13].

1.4 Thesis Statement

The objective of this research is to design, develop and implement a hierarchical

control system that assures stability in a power system interconnected. These

include the management and control algorithms withon-line measurements.

1.5 Research Goals

The focus of this thesis is the design of a hierarchical control applying remote

feedback signals used for the damping of LOF, inter-area oscillations in a power

systems proposed as well which is composed by 6-machine/ 3-areas. A

methodology based on eigenanalysis is derived to locate the local controllers in

the test system.

Also demonstrated is the resulting tie-line power transfer gain due to the

damped oscillations. Finally, time-domain simulations performed on the test

system will be employed to study the nonlinear response following large

disturbances.

16

1.6 Organisation of the thesis

The remainder of this document is organised as follows: in chapter2, it is

analysed the study benchmark system and obtained the PSS and PMU location.

In chapter3, the on-line measurements algorithms are described and tested. In

chapter4 is shown the local controller and it is presented the hierarchical

controller design based multi-agents. In chapter5, it is compared the action of

the proposed hierarchical control strategy with the local controllers only.

Finally, the conclusions are presented and a future work is given.

17

2. PMU AND PSS LOCATION

In the context of transmission grids, Small Signal Stability Analysis and in

particular, the analysis of inter-area oscillations becomes more and more

important. Many electric systems worldwide are experiencing increased

loading on portions of their transmission systems that can, and sometimes do,

lead too poorly damped, low frequency (0,2-0,8 Hz) inter-area oscillations. This

topic has been extensively addressed for long time in conjunction with power

systems for which the extension of the grid and the high level of power

transfers led to stability problems. Inter-area oscillations can severely restrict

system operations by requiring the curtailment of electric power transfers as an

operational measure. These oscillations can also lead to widespread system

disturbances if cascading outages of transmission lines due to oscillatory power

swings.

The eigenvalue analysis investigates the dynamic behaviour of a power system

under different characteristic frequencies (“modes”). In a power system, it is

required that all modes to be stable. Moreover, it is desired that all

electromechanical oscillations to be damped out as quickly as possible. The

results of an eigenvalue analysis are given as frequency and relative damping

for each oscillatory mode to make them easier to understand.

In addition, the modal analysis allows a much deeper view of a system by not

only interpreting the eigenvalues but by analysing the eigenvectors of a system,

which are automatically calculated during the modal analysis:

- The right eigenvector gives information about the observability of

oscillation.

18

- The left eigenvector gives information about the controllability.

- The combination of right and left eigenvectors (residues) indicates the

controllers’ settings.

2.1 Power System Proposed

In order to study the control structure proposed it is derived a power system

from [42], with some modifications. The original system is presented inFig.1.

Fig.1 Derived Power System [42]

The system proposed is conformed by 3-areas; each of the areas has two

generators, and links with weak tie-lines. The generators are identical and

modelled with 6 state variables and the Automatic Voltage Regulators (AVR)

are also identical and represented by 2 state variables. The system is shown

inFig.2.

19

Fig.2 Power System Proposed

2.2 Eigenvalue Analysis

There are many possibilities to locate the PSS along the AVR of the system and

the PMU as well. To face the problem of knowing the optimum site, it is applied

a well-known methodology based on the Small Signal Stabiliy Analysis (SSSA),

particularly the controllability analysis [43][45].

The eigenvalues of weak damping influence in a high way the dynamic stability

of power systems and the eigenvalues of lower frequencies deal with the inter-

area power oscillations. Table.I and Table.II show two dominant eigenvalues

and participation factors given by the analysis.

Table.I System Eigenvalues

. No. R. Part Im. Part Frequency Damp.

1 -0.58 6.99 1.12 8.3%

2 -0.69 6.89 1.10 8.5%

3 -0.61 6.64 1.06 9.1%

4 -0.26 4.75 0.76 5.5%

5 -0.23 4.17 0.66 5.4%

20

2.3 Participation Factors

The participation factor element gives a measure of the kth state variable in aith

mode, and vice versa.

Table.II Participation Factors

No. G1 G2 G3 G4 G5 G6

1 33% 6% 2% 1% 47% 3%

2 2% 42% 21% 6% 2% 18%

3 7% 10% 35% 27% 3% 10%

4 0% 13% 12% 31% 0% 38%

5 36% 5% 5% 12% 27% 10%

FromTable.I, it can be seen that the oscillatory modes are mainly 4 and 5,

however from Table.II it is not totally clear where the PSS might be located.

2.4 Mode Shapes

The mode shape is the response of a particular oscillatory mode in the right

eigenvector.

When the Mode Shapes (MS) of the critical modes (4 and 5) are plotted (Fig.3

and Fig.4), it can be inferred that there is an inter-area oscillation due to

generators G2 and G6 against G3 and G4; in other words, area 1 vs. area 3. In

the other critical mode, generators G1 and G5 are oscillating against G4 and G6

mainly, so there is another inter-area oscillation (area 2 vs areas 1&3).

21

Fig.3 Mode Shape Mode 4

Fig.4 Mode Shape Mode 5

To guarantee the observation of those modes in the mentioned areas the PMUs

are located in the correspondent tie-lines, especially in the buses 5 and 12.

2.5 Controllability Analysis

The controllability index of the system shows that the PSS might be located in

G1, G4 and G6 as it is depicted in Fig.5 and Fig.6.

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Mode 4: 0.75658 Hz

Axis x

Axis y

Gen1

Gen2

Gen3

Gen4

Gen5

Gen6

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Mode 5: 0.66342 Hz

Axis x

Axis y

Gen1

Gen2

Gen3

Gen4

Gen5

Gen6

22

Fig.5 Controllability Mode 4

Fig.6 Controllability Mode 5

According to the analysis shown above, Fig.7 shows which of the PSS are

activated and where the PMU are placed.

1 2 3 4 5 60

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6Controlability Mode 4

Machines

1 2 3 4 5 60

0.2

0.4

0.6

0.8

1

1.2

1.4Controlability Mode 5

Machines

23

Fig.7 PSS and PMU Location

2.6 Voltage Stability Analysis

Once it is located the PMU and PSS, it is necessary to establish how it can be

critical a fault in any line in the system. In order to identify the most critical

line(s) of the power system, all possible single contingencies (n-1) are simulated,

obtaining that the outage of all lines caused the system instability. The voltage

stability shows that any disturbance in any line in the system can generate

instability (Fig.8).

Fig.8 Voltage Stability Analysis

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Loading Parameter (p.u.)

24

What is more thesystem shows a critical mode in bus 6 as it can be seen in Fig.9.

andTable.III.

Fig.9 Stability Plane

Table.III Eigenvalue Analysis in Buses

Eigevalue Most Associated Bus Real part

EigJlfr # 1 Bus 05 148,9455

EigJlfr # 2 Bus 12 96,48864

EigJlfr # 3 Bus 06 3,66359

EigJlfr # 4 Bus 07 56,24301

EigJlfr # 5 Bus 04 912,2962

EigJlfr # 6 Bus 13 766,825

EigJlfr # 7 Bus 14 630,2312

EigJlfr # 8 Bus 02 650,041

EigJlfr # 9 Bus 08 721,5046

EigJlfr #10 Bus 10 622,1418

EigJlfr #11 Bus 01 999

EigJlfr #12 Bus 03 999

EigJlfr #13 Bus 09 999

-200 0 200 400 600 800 1000-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Real

Imag

25

Eigevalue Most Associated Bus Real part

EigJlfr #14 Bus 11 999

EigJlfr #15 Bus 15 999

EigJlfr #16 Bus 16 999

In the remainder of this document the fault considered is going to be located in

bus number 6 in order to analyse the system transient stability. Thus, a 500 ms

fault is simulated at in bus 6 (between areas 1 and 3), obtaining oscillations of

important magnitude in voltage and power.

26

3. ON-LINE MEASUREMENTS

Real-time monitoring and identification of the characteristics of inter-area

oscillations, including damping factors and frequency oscillations, is a

prerequisite for applying corrective measures for system stabilisation in large

power systems. A wide area measurement (WAM) approach based on the use

of Synchronised Measurement Technology (SMT) leads to more efficient

damping of inter-area oscillations as well as two main functions: the first is to

collect, monitor, manage, and maintain the real-time synchronised phasor data

and real-time switching information uploads from the PMU measurement

substations; the second is to diagnose wide area faults, judge protection logic,

and issue control orders (such as tripping or blocking) to the control

substations[46].

In the last two decades, a number of solutions for online monitoring and

identification of power system oscillation modes were presented in scientific

literature. After detecting inter-area oscillations, damping and frequency

components are commonly determined by applying methods based techniques,

or approaches based on different parameter estimation methods.

Phasor estimation is an important issue that has been in continuous

development and some important advances have been done concerning

measuring power oscillations [47].

In this document are proposed and developed three real-time measurements

bearing in mind the implementation of control actions. These measurement

algorithms are presented in detail below.

27

3.1 Damping Algorithm Proposed

Taking into account that this approach looks for a maximum damping in the

tie-lines, an algorithm is developed to measure the damping factor of any

signal, not only a power signal but supposing that the signal is already filtered,

as it can be seen in Fig.10.

Fig.10 Damping Algorithm

In order to test the algorithm it is simulated a typical signal in Simulink® with

the next characteristic equation (1), knowing the constant parameters.

(1)

where,

28

k= amplitude,

w= angular frequency,

This algorithm is embedded in Simulink blocks as it is shown in Fig.11.where it

is added a zero block crossing detector of the derivative signal knowing the

signal peaks and a the exact time when it happens.In addition, some delays are

added as well, in order to give some initial conditions to the whole variables in

the simulation.

This algorithm is able to measure the damping factor and the relative amplitude

(overshoot) however the last is not applied in this document.

Fig.11 Damping Algorithm Embedded

2

Overshoot

1

Damping

Zero

CrossingCnt

Zero Crossing

In1

In2

In3

In4

In5

In6

In7

In8

In9

In10

In11

In12

In13

In14

In15

In16

In17

In18

In19

In20

In21

Out1

Out2

Out3

Out4

Out5

Out6

Out7

Out8

Out9

Out10

Out11

Out12

Out13

Out14

Out15

Out16

Out17

Out18

Out19

Out20

Out21

Subsystem

Scope1

t

maxmin

senal

start

ndatos

promdatos

t1

t2

b

amp1

amp2

ov

delta1

dt1

amp3

amp4

t3

t4

delta2

c

f

dt2

h

amort

ov er

damp

start_out

ndatos_out

promdatos_out

t1_out

t2_out

b_out

amp1_out

amp2_out

ov _out

delta1_out

dt1_out

amp3_out

amp4_out

t3_out

t4_out

delta2_out

c_out

f _out

dt2_out

h_out

amort_out

damping

Embedded

MATLAB Function

du/dt

Derivative

0

Clock

1

Signal

29

The final simulink block implemented is presented in Fig.12 where there is an

input which is the signal and two outputs: the damping factor and the

overshoot.

Fig.12 Damping and Relative Amplitude Measurement Block

Fig.13 shows the signal introduced and the damping factor obtained which

corresponds with the data assigned.

Fig.13 Damping Measurement

2

Out2

1

Out1

SignalDamping

Ov ershoot

Subsystem1

if { }

Action Port

1

In1

0 1 2 3 4 5 6 7 8 9 10-4

-2

0

2

4

6

8

10

12

14

Time(s)

Sig

nal

0 1 2 3 4 5 6 7 8 9 10-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

Time(s)

Dam

pin

g

30

3.2 Relative Amplitude and Frequency Measurements

During the search of real-time measurements, other algorithms are developed

in order to study the most suitable measure keeping in mind the controller

action and objectives.

As it is mentioned and shown above, it is developed an algorithm able to

measure the relative amplitude, moreover, another measurement is

implemented and tested in simulink.

Taking into account the necessity to measure the frequency of a signal,

especially the low frequencies it is designed another embedded algorithm and

presented in simulink blocks (Fig.14).

Again this algorithm, takes advantage of the signal derivation and the zero

crossing to detect the peaks. The clock input plays a key role as well, giving the

time footprint in real time.

Fig.14 Frequency Measurement Simulink Diagram

1

fZero

CrossingCnt

Zero Crossing

1/z

Unit Delay2

1/z

Unit Delay1

1/z

Unit Delay

Scope2

Scope1

Scope

Product

t

dato

t1

t2

t3

f

t1s

t2s

t3s

conteo

Embedded

MATLAB Function

Display2

Display1

du/dt

Derivative

2*pi

Constant

0

Clock

1

Signals

31

The implemented block in simulink has one input (the signal) and one output

(the frequency) (Fig.15).

Fig.15 Frequency Measurement Block

Even though the algorithm provides a correct frequency measurement; in

general terms it does not give enough information to do a control action

because the frequencies associated to the electromechanical modes are too

similar and in a similar range.

3.3 Signal Dynamic Deviation

As it is shown below, another measurement is going to be included to the

correspondent control action that measures the signal deviation, and in this case

the damping factor deviation (Fig.16).

To achieve a measurement deviation it is built a simple block diagram to carry

out this achievement as a discrete function expressed as follows:

(2)

Fig.16 Signal Deviation Implementation

1

Out1

Signals f

Subsystem

if { }

Action Port

1

In1

1

Out1

1/z

Deviation

1

In1

32

3.4 Algorithms Tests

All the algorithms developed are tested in the power system implemented in

the PMU locations mentioned in Section II. The simulation is run including the

selected PSS. The next figures are organised following aspecialorder, in the first

row is presented the main measurement (damping, relative amplitude or

frequency), in the second row is shown their deviation and finally, the original

signal is presented.

3.4.1 Damping Factor Measurement

The damping algorithm recognises the negative damping as the increasing of

signal, for that reason appears different steps ofmeasurement. In other cases

even the signal seems to be in steady state the measurer indicates the minimal

changes in the damping and continuous changing as it can be seen in Fig.17 and

Fig.18.

Fig.17 Damping Measurement between areas 1 and 2

0 5 10 15 20 25 30 35 40 45 50-1

0

1

Time(s)

Dam

pin

g F

acto

r

0 5 10 15 20 25 30 35 40 45 50-2

0

2

Time(s)Dam

pin

g F

acto

r D

evia

tion

0 5 10 15 20 25 30 35 40 45 500

500

Time(s)

Ele

ctri

cal

Pow

er

33

Fig.18 Damping Measurement between areas 1 and 3

3.4.2 Frequency Measurement

Due to variations mentioned above, there are a lot of high frequency

components which the algorithm measure. It is necessary to remember that the

frequency measurements related with the electromechanical oscillations are in

the range of 0.1 to 2 Hz which are not clear because of the higher values of

frequency. This effect is shown Fig.19 and Fig.20.

Fig.19 Frequency Measurement between areas 1 and 2

0 5 10 15 20 25 30 35 40 45 50-1

0

1

Time(s)

Dam

pin

g F

acto

r

0 5 10 15 20 25 30 35 40 45 50-2

0

2

Time(s)

Dam

pin

g F

acto

r D

ev

iati

on

0 5 10 15 20 25 30 35 40 45 50200

400

600

Time(s)

Ele

ctr

ical

Po

wer

0 5 10 15 20 25 30 35 40 45 500

50

100

Time(s)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 50-100

0

100

Time(s)

Fre

qu

ency

Dev

iati

on

0 5 10 15 20 25 30 35 40 45 500

500

Time(s)

Ele

ctr

ical

Po

wer

34

Fig.20 Frequency Measurement between areas 1 and 3

3.4.3 Relative Amplitude Measurement

Finally, the relative amplitude measurement shows an adequate response even

though the oscillations presented in the tie-lines.

Fig.21 Relative Amplitude Measurement between areas 1 and 2

0 5 10 15 20 25 30 35 40 45 500

50

100

Time(s)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 50-100

0

100

Time(s)

Fre

qu

ency

Devia

tio

n

0 5 10 15 20 25 30 35 40 45 50200

400

600

Time(s)

Ele

ctr

ical

Po

wer

0 5 10 15 20 25 30 35 40 45 500

200

400

Time(s)

Rel

av

ite A

mp

litu

de

0 5 10 15 20 25 30 35 40 45 50-500

0

500

Time(s)

Rel

av

ite A

mp

litu

de

Dev

iati

on

0 5 10 15 20 25 30 35 40 45 500

500

Time(s)

Ele

ctri

cal

Pow

er

35

Fig.22 Relative Amplitude Measurement between areas 1 and 3

0 5 10 15 20 25 30 35 40 45 500

500

1000

Time(s)

Rela

vit

e A

mp

litu

de

0 5 10 15 20 25 30 35 40 45 50-500

0

500

Time(s)

Rela

vit

e A

mp

litu

de

Dev

iati

on

0 5 10 15 20 25 30 35 40 45 50200

400

600

Time(s)

Ele

ctr

ical

Po

wer

36

4 MULTI-AGENT SYSTEM DESIGN

A Multi-Agent System (MAS) is a system that consists of several coordinating

and computing entities called "agents". There are many definitions for an agent.

The agents may be software agents, such as computer programs or they may be

people like us. An agent might be working alone in an environment or it may

communicate, coordinate and share with other agents to achieve its assigned

goals[48].

The hierarchical control proposal based on MAS, use a mixed

centralized/distributed planning coordination scheme due to each agent has its

inner actions and besides, there is a central planner which conceives the

organization actions of the local agents [49]. As it can be seen in Fig.23, the

identified processes are associated to the local PSS which are governed by their

local supervisors and the upper layer commands the local controllers.

Fig.23 Hierarchical Controller Concept

37

4.1 Application Design

In this step is defined the design process as refinement responsibilities

identified. Refinement is performed by mapping responsibilities to generalised

problems and choosing the most appropriate solution, refinement process

involves two steps:

1. Knowledge Modelling, and

2. Task Roles

4.1.1 Knowledge Modelling

AVRn GnVstabFL-PSSn

Local

Measurement

Hierarchical

Takagi-Sugeno

Fuzzy Logic

(HTSFL)

PSS

Remote

Measurement Power System

Network

Coeffient

weights

AVRi GiVstabFL-PSSi

Local

Measurement

Fig.24 Proposed Controller Scheme

The first stage in the application design process is the knowledge modelling.

This knowledge uses the agent roles defined by fuzzy control rules.

38

The proposed structure of control is based on a centralised TSO which receives

the Remote Feedback Signals (RMS) from a PMU located in the power network

and sends back some command signals to the correspondent local controller in

charge of the generators. In this case, the hierarchical control is based on Takagi

Sugeno Fuzzy control. Fig.24 depicts the proposed hierarchical controller

scheme.

4.1.1.1 Local Control Model

The fuzzy design is based on a previous design presented in detail in [50][51],

where it is explained and justified the membership functions, the fuzzy

inference and defuzzification method, so it is summarised and validated now in

order to test it with the hierarchical controller exposed below.

Input variables are the speed deviation and active power deviation. The

meanings of abbreviations are BN=Big Negative, MN=Medium Negative,

LN=Low Negative, Z=Zero, LP=Low Positive, MP=Medium Positive and

BP=Big Positive and all of them are trapezoidal functions. Fig.25shows the

input membership functions. These inputs have the same functions because are

dependant as it is expressed in (3):

(3)

39

Fig.25 Speed deviation and Power Active deviation Input Memberships

functions representation

The output membership function which is the AVR input is depicted in Fig.26.

Fig.26 Output Membership Functions

-5 -4 -3 -2 -1 0 1 2 3 4 5

0

0.2

0.4

0.6

0.8

1

dw

Deg

ree o

f m

em

bers

hip

BNMN

LNZero

LPBP

MP

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Vstab

Degre

e o

f m

em

bers

hip

BN MN LN BZLZ

LP MP BP

40

Fuzzy rules describe the controller output based on the speed deviation (Δw),

and active power deviation (ΔPa) inputs; there are (7x7) 49 rules according to

the ranges of the variables of the multimachine system [50]. The decision table

is shown in Table IV.

Table.IV Decision Rules

∆w/∆Pa BP MP LP Z LN MN BN

BN BZ LN MN MN BN MN BN

MN LP BZ LN MN MN BN BN

LN MP LP BZ LN LN MN BN

Z BP MP LP LZ LN MN BN

LP BP MP LP LP BZ LN MN

MP BP BP MP MP LP BZ LN

BP BP BP BP MP MP LP BZ

4.1.1.2 SupervisorControl Model

Keeping in mind that the idea of the hierarchical controller is to obtain the

maximum damping as it is possible and taking the damping algorithm

measurement presented above, it is designed a Takagi Sugeno approach which

commands the local PSS signals (Fig. 27).

The fuzzy inference system uses as antecedent the selected measurement of the

damping in the tie-lines and the damping deviation in order to get qualitative

information of the signal and to give more robustness to the controller. In

addition, dynamic pre-filters are used to obtain the damping deviation.

41

The linguistic labels associated to the damping are similar to the membership

functions of the local controllers, however for the damping deviation there is a

high variation with the next fuzzy subsets: BNC=Big Negative Change,

LNC=Low Negative Change, LPC=Low Positive Change, BPC= Big Positive

Change. The membership functions are presented in Fig.29 and Fig.29.

Fig. 27TakagiSugeno Control Proposed

Fig.28 Input Membership Functions of the Hierarchical Controller

System TSdp

c: 2 inputs, 2 outputs, 20 rules

Damping Factor (5)

Damping Factor Deviation (4)

f(u)

PSS1 (2)

f(u)

PSS2 (2)

TSdp

c

(sugeno)

20 rules

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Dp

Deg

ree

of

mem

ber

ship

BN Z BPLN LP

42

Fig.29 Input Membership Functions of the Hierarchical

Controller(Deviation)

The control surface associated is shown in Fig.30.

Fig.30 Surface Control Associated

The output membership functions are adjusted finding the minimum error in

steady state and the minimum oscillation in transient state by trial and error

method using the toolbox FIS of Matlab®.

The rule base that represents the knowledge obtained from the behaviour of the

system is summarised in Table.V. The rule base of the controller is proposed

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

DpDev

Deg

ree

of

mem

ber

ship

LPCBNC LNC BPC

-1

-0.5

0

0.5

1 -1

-0.5

0

0.5

1

0

0.2

0.4

0.6

0.8

1

Damping Factor DeviationDamping Factor

PS

S1

43

after getting a previous knowledge about the dynamic and steady state

behaviour of the system. The output of the fuzzy controller is to give weighted

factors with different scales according to the damping measurements which

indicates what local fuzzy controller should be adjusted, and based on that a

damping factor near to the unit is the best option and it happens when the

oscillations are mitigated. In the case that the damping measurement gives a

low factor, the hierarchical controller orders the most weighted action to the

local controllers.

A negative damping factor can appear, and it is presented when there are low

oscillations and change to high oscillations, which is the case of a fault and the

action is contemplated with the same high weight. In that order, the linguistic

labels are only positive in three scales: Small (S), Medium (M) and Large (L).

Table.V Decision Rules

∆dp/dp BN LN Z LP

BNC PL1 PL2 PM1 PS2 PL1 PL2 PL1 PL2

LNC PL1 PM2 PM1 PM2 PL1 PL2 PM1 PM2

LPC PM1 PS2 PL1 PM2 PL1 PL2 PM1 PL2

BPC PM1 PS2 PS1 PS2 PL1 PL2 PL1 PM2

4.1.2 Tasks Roles

Designing a multi-agent system for any system is based on certain rules, and

requires classification of component agents, their characteristics, extent of

influence and limitations. The idea behind any multi-agent system is to break

down a complex problem that being handled by a single entity into smaller

simpler problems handled by several entities. Based on the goals a multi-agent

44

system designed for WAMCS should be able to accomplish the following three

tasks.

4.1.2.1 User Access Task

To provide user gateway that make features of the Smart grid accessible to

humans. It includes responsibility of providing users with real-time

information of entities residing in the system, by displaying active power

measurements and the remote/local feedback signals and their status.

4.1.2.2 Control task

This task includes responsibilities of monitoring the active power in selected

buses of the main grid in ordert to detect their fluctuations and apply the

correspondant control actions.

4.1.2.3 Measurement Task

This task has the responsibility of monitoring active power buses, speed rotors

or frequency and controlling their (on-off) status.

4.1.2.4 Coordination Task

While task distribution is one of the important rules played by a multi-agent

system, there is no-need to distribute the activities as widely as possible. The

application of multi-agent systems can be more efficient if some agent's

activities are centralised. That provides efficient by:

45

- Reducing the number of messages exchanged among agents and

- Simplifyng the overall complexity of multi-agent system implementation

The central control has a typical hierarchical fuzzy structure based on the

observation system and derived from simulations to form knowledge base

consisting of if – then rules[52]. One advantage of using fuzzy rules is the

linguistic variables applied which is a straightforward way to describe a

behaviour system.

A fuzzy rule base is expressed as:

if<fuzzy proposition>, then<fuzzy proposition> (4)

The propositions are combinations of conditional, unconditional and assigned

statements, the relationship of these sets of rules, the nested loops and the

priority composition form the central control.

The fuzzy control levels specify three actions listed as follows:

1. The regulation of steady – state power error: an important aspect

proposed, is the requirement in the coordination of the voltage

stabilisation references changes when is required and involves the

primary control level explained above. Based on this desired

behaviour, the corresponding fuzzy rule is:

- if the speed deviation and active power deviation changed in a

PSSn, then change the voltage stabilisation and apply the fuzzy local

control rules.

46

2. The secondary control target has to coordinate the whole PSS

involved at one set point or to adjust different set points.The overall

power and the comparison with the reference set point are measured

and then is assigned a weight according to the error difference

measurements in each PSS, determining how close are (over or down)

to the global speed set point and finally apply the local control. In

terms of fuzzy logic this rule can be written as:

- if the power measurement p in a unit tie-line is not damped then assign a

weight wd to increase or decrease the local settings.

4.1.2.5 Bulletin Board Tasks

This is a special task which represents a dynamic contact point through which

all entities share and retrieve information. This requires a decision support

system that would enable look-ahead optimal settings during both emergency

(when there is fault) and non-emergency conditions.

4.1.3 Agent Specification

In this step, specifications regarding agents belonging to the multi-agent system

are defined.

4.1.3.1 Control Agent

47

Control agent receives the power measurement from the measurement agent

and applies the most optimal control action possible and is responsible to

redistribute the local PSS actions.

4.1.3.2 User Agent

Provides the interface, monitor the power measurements and acctions. User

agent can re-define the control actions.

4.1.3.3 Monitor Agent

Gives the information to all the other agents and

4.2 Constraints

Constraints mean restricting the values of concepts to subsets of legal values.

No constraints need to be ascribed to concepts for the applications. Default

values for all concepts have been identified. The application does not require

any types to be related to the concepts.

48

5. MULTIAGENT SYSTEM APPLICATION AND STUDY CASE

In this thesis, the smart transmission grid is developed in Matlab/Simulink

environment to demonstrate the proposed multi-agent system functionality.

The implementation of multi-agent systems is of core importance.

This chapter addresses the study case conducted to analyse the multi-agent

systems functionality of reducing the damping in the tie-lines of the power

system studied applying on-line measurements under a wide area

measurement and control framework (Fig.31).

Fig.31 Multi – Agent Framework

The system is implemented in Simpower® and simulated without the PSS signal

in any machine, a three phase fault point as a disturbance is inserted in bus 6,

which is in the middle of the areas 1 and 2, with duration of five cycles and then

it is cleared as it is shown inFig.32 .

49

The case studies conducted for the demonstration of multi-agent system

functionalities. The results and description of the tests are presented and

discussed at next.

Fig.32Simpower System Implementation

5.1 Transient Stability

Fig.33 shows the time response of electrical power of each machine and can be

seen clearly that the system presents a critical stability and inter-area

oscillations.

yellow=M1,

magenta=M2,

cyan=M3,

red=M4

Green=M5

Blue=M6

Select a specific PSS model

by typing:

0 (No PSS)

1 (MB-PSS)

2 (Delta w PSS from Kundur)

3 (Delta Pa PSS)

Phasors

Vps

P_B1->B2

System

Data

System

Show results:

3-phase fault

Show results:

Step on vref

of M1

Show Bode plot

of PSS

STOP

Stop simulation

if loss of synchronism

2

PSS model

Machines2

Machines1

Machines

d_theta

w

d_theta1

Pe

Stop

Machine

Signals

Line 2-3

(110 km)1

Line 2-3

(110 km)

Line 2

(220 km)

Line 1b

(110 km)

Line 1a

(110 km)

PSSmodel

Goto

A B CA B C

Fault

Electrical Power

Yellow=M1 mag=M2 cyan=M3

red=M4 Green=M5 Blue=M6

?

Double click here for more info

Control System

A

B

C

a

b

c

Brk1

A

B

C

a

b

c

B4

A

B

C

a

b

c

B3

A

B

C

a

b

c

B2

A

B

C

a

b

c

B1

A

B

C

D

E

F

Area 3

A

B

C

D

E

F

Area 2

A

B

C

D

E

F

Area 1

A

B

C

a

b

c

Brk2

d_theta v s M6 (deg)

w (pu)

Pos. Seq.

V_B1 & V_B2 (pu)

Activ e Power f rom

B1 to B2 (MW)

50

Fig.33 Electrical Power Time Response of Generators (Local)

Fig.34 shows the oscillations in the tie-line which interconnects area 1 and 2.

Fig.34 Electrical Power Time Response of a Tie-line (Remote)

Now the system is simulated with only two local PSS and with the hierarchical

control proposed and it is observed the power flow in the tie-lines which

interconnect the area 1 with 2 and area 2 with 3 respectively. Even the

oscillations are mitigated with the local PSS (in green and blue), the hierarchical

20 25 30 35 40 450

0.2

0.4

0.6

0.8

1

1.2

Time(s)

Ele

ctri

cal

Pow

er (

MW

)

G1

G2

G3

G4

G5

G6

20 25 30 35 40 45 50140

160

180

200

220

240

260

280

Time(s)

Ele

ctri

cal

Pow

er (

MW

)

51

controller presented achieves a minimal oscillation after the first swing in both

cases (in blue and red), and the HPSS is more effective for inter-area damping.

Fig.35 Electrical Time Response in the Tie-lines with LC (Local Controllers) and

HC (hierarchical controllers)

Fig.36 Electrical Time Response in the Tie-lines with LC (Local Controllers) and

HC (hierarchical controllers)

The selection of local fuzzy controllers instead of conventional controllers is

based on its adaptability. A test is done while the hierarchical controllers are

used with both types of local PSS.

20 25 30 35 40 45 50100

150

200

250

300

350

400

Time(s)

Ele

ctri

cal

Pow

er (

MW

)

20 25 30 35 40 45150

200

250

300

350

400

450

500

550

Time(s)

Ele

ctr

ical

Po

wer

(MW

)

52

Fig.37 shows the power electrical measurements in generators when is used the

hierarchical controller and Fig.38 shows the same signals when is used the

fuzzy local controllers and the hierarchical control at the same time. It can be

seen that, when are used the fuzzy local controllers case those fluctuations are

reduced.

Fig.37 Electrical Power Time Response of Generators with conventional PSS as

long as it is applied the Hierarchical Control

Fig.38 Electrical Power Time response of Generators with the two fuzzy

controllers

25 30 35 40 45 500.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time(s)

Ele

ctr

ical

Pow

er

(MW

)

G1

G2

G3

G4

G5

G6

20 25 30 35 40 45

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time(s)

Ele

ctr

ical

Pow

er(

MW

)

G1

G2

G2

G4

G5

G6

53

The zoom in shown in Fig.39 Power Response Zoom in with Fuzzy Local

Control demonstrates the fuzzy controller performance reducing the power

fluctuations.

Fig.39 Power Response Zoom in with Fuzzy Local Control

5.2 Results and Discussion

In order to evaluate the dynamic response of the controller proposed, the

damping factor (ζ) measured and the settling time (st), are compared with the

local controller. Table IV the advantages of the hierarchical controller.

Table.VI Controller Comparison

Local Controllers Hierarchical Controller

ζ (%) 0.1 0.65

st (s) 15 8.33

Once the measurement agent detects the fault at t= 25s, the control agent

informs the user agent and the control agent, both of which exchange

30 31 32 33 34 35 36 37 38 39 40

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

Time(s)

Ele

ctri

cal

Po

wer

(W)

G1

G2

G2

G4

G5

G6

54

information and determine the optimal weight and the amount of power to

stabiliser the power system. The control coordination agent sends a control

signal to the local control agent to change the settings.

The use of the hierarchical control achieves to maximise the damping factor in a

minimum time compared with the use of the local controllers only.

55

6. CONCLUSIONS

The eigenvalue analysis is a traditional method for offline analysis of dynamic

properties of power systems. It is based on the assumed system model and the

classical methods of linear systems control theory. With the help of eigenvalues,

eigenvectors, and participation factors, the system characteristics can be

predicted. However, this hardly meets the severe requirements for efficient

monitoring of dynamically changed power systems possessing a high level of

uncertainty. The system topology and the states are dynamically changed. The

system parameters are not constant.

The hierarchical controller methodology based on Takagi Sugeno approach

involving on-line measurements demonstrates a suitable time response

obtaining a damping factor maximised.

The wide area on-line measurements proposed gives an advantage over the

local measurements in order to reduce the LOF in the main tie-lines involved

and can be easily embedded in software/hardware systems.

The adaptability of the fuzzy logic controllers in Smart Transmission Grids

provides to be a good solution for the next generation of control systems

involved.

56

7. FUTURE WORK

This kind of control can be applied in larger systems however it is necessary to

reduce the computational time in terms of simulation and future

implementation.

A digital signal processing analysis can be required to reduce the external noise

in measurements in field system.

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