EPCC Workshop 2019 - 15 EPCC Workshop - May 12 – 15, 2019 - … · 2016. 11. 7. · ©2015 Mladen Kezunovic, All Rights Reserved Processing weather and power grid data using advanced

©2015 Mladen Kezunovic, All Rights Reserved

Processing weather and power grid data using advanced data analytics and GIS framework

Mladen Kezunovic, Ph.D., P.E.

Texas A&M University

May 20, 2015


• What: The problem

• Why: The background

• How: The data and analytics

• When: The spatial/temporal

focus

• Applications (Examples)

Outline


What: The problem

Reported power outages by cause in Texas in 2013.

Blackout Tracker United States Annual Report 2013, Eaton, 2014..

• Weather factor is the main reason for outages (i.e. falling

trees on the transmission lines in overhead systems).


What: The problem






focus


Outline


• Weather patterns are different lately

• Weather data is more elaborate now

• Weather impacts are more prominent

• The forecast analytics are more advanced

• Grid impact analysis is more sophisticated

• Grid overlay is more integrated using GIS

• The actions may be more predictive

Why: The background


Why: The background

Three directions

• Data integration

• Risk assessment

• Time/space (GIS)

prediction






focus


Outline


Risk Assessment Framework

Where:

R Is the Risk

P[T] Is the Hazard: Probability of a Threat [ T ]

P[C|T] Is the Vulnerability: Probability of the Consequences C given the threat intensity T

u( C ) Is the utility of the Consequences ( C )

Assumptions:

•Risk R is defined for a particular threat T intensity defined on a particular point in

space and time

• Vulnerability is conditioned on a particular threat intensity T

•Consequences C assessment are assumed to be certain (may be uncertain as well)

Risk Hazard Worth of Loss = x

R P[ T ] u( C ) = x

Vulnerability x

P[ C | T ] x

9


Risk Assessment via Bayesian Networks

Risk Hazard Worth of Loss = x

R P[ T ] u( C ) = x

Vulnerability x

P[ C | T ] x

T C R

Hazard Vulnerability Risk

Hazard Vulnerability Risk






focus


Outline


Spatial/temporal state of risk


Risk for the grid






focus


Outline


Fault Location

Lightning

Detection

Network

Date and time of

lightning strike,

Tlight

Location of a strike

(latitude and

longitude), Llight

Peak current and,

Ilight

Lightning strike

polarity, Plight

Type of lightning

strike (cloud to

cloud or cloud to

ground), Typelight

Traveling Wave

Fault Locators

Date and time

when event was

recorded, TA and

TB for two devices

Distance to the

fault from the line

terminal A, θA

Transient signals

recorded at the

line terminals

Geography

Location of substations

Geographical

representation of the

line

Simulation

Transmission line parameters

Physical characteristic of a

transmission line and towers

Line length,

Dynamic Static

l

Methodology


Fault Location

Results

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

50

100

150

TRAVELING WAVE

LIGHTNING DATA

PROPOSED APPROACH

Histogram of an error distribution for individual

traveling wave and lightning data; and our approach

that combines two methods

• Our approach shows better accuracy than the individual methods in all test cases

• The variance and the mean of the error were smaller using the improved method.

Mean Square Error:

• Lightning:

0.0076±3.1e-04 miles,

• Traveling wave:

0.0012±4.3e-05 miles,

• Combined approach:

0.0011±4e-05 miles.


Insulation coordination

Data

Lightning

Detection

Network

Weather Insulation

Studies Geography

Traveling

Wave Fault

Locators

Date and

time of

lightning

strike

Temperature

Surge

impedances

of towers

Location of

substations

Date and

time when

event was

recorded

Location of

a strike

Atmospheric

pressure

Surge

impedances

of ground

wires

GIS

representati

on of the

line

Distance to

the fault

from the

line

terminals

Peak

current

and strike

polarity

Relative

humidity

Footing

resistance

Location of

towers Transient

signals

recorded at

the line

terminals Type of

lightning

strike

Precipitation Components

BIL

Location of

surge

arresters

Methodology

Select one

lightning strike

from the table

Set fault

parameters based

on lightning data

Run simulation in

ATP EMTP

Record measured

voltages

Compare measured

voltages to BIL

Get BIL for

the faulted

line

Calculate

nonstandard

BIL based on

weather data

Add data to the prediction model

Select faulted line

Repeat

until

lighting

data table

is empty


Insulation Coordination


SUB1

T1_1 T1_N

T2_1 T2_N

T3_N T3_1 …

SUB3

SUB2

…

T1_N

T1_N

SUB4

. .

.

SUB – Substation

T – Tower

MS – Meteorological station

– Measurement

Nodes: X = (Lig_Curr, Temp, Press, Hum, Prec, BIL_old)

Y = (BIL_new)

Branches: Impedance matrix

MS1

MS2

From MS


Outage Management Processes

DATA INPUT

Weather Data:

Precipitation, Wind

Speed.

Vegetation Data:

Canopy Height.

Electrical Data:

AMI, SCADA,

PMU, etc.

Other Data:

Customer calls

ANALYSIS AND

OUTAGE

MINIMIZATION

Outage mapping

classification and

initial crew dispatch

PHYSICALLY

SEARCH FOR

OUTAGES

Work/Crew/Dispatch

Management

FAULT LOCATION

ANALYSIS

Computer Running for

identifying precise

fault locations

RESTORATION

Fault

isolation/switching/ re-

energize the feeder

POST-EVENT

DOCUMENTATION

GIS

Database

Electrical

Database


Distribution outage prediction

Overhead distribution network Wind data of southeast region of USA

Global vegetation

data


Data Integration

Power system and wind

data (higher wind speed at

right hand side)

Power system and canopy

height data (darker green

for larger canopy height)

Identify the zones having

higher wind speed and

larger canopy height data


• Suppose the zone with highest probability of outage is located

(tree fall due to wind).

• Assume in the real-time operations, an operator would like to

find the precise locations using IED measurements.

GIS to Electrical Database

GIS

Database

Electrical

Database


• R. A. F. Pereira, et al., "Improved Fault Location on Distribution Feeders Based on Matching During-Fault Voltage

Sags,“ IEEE Trans. Power Del., Vol . 24., No. 2, pp852-862, Apr. 2009

• S. Lotfifard, M. Kezunovic, M. J. Mousavi, "Distribution Fault Location Using Voltage Sag Data" IEEE Trans. Power

Del., Vol. 26, No. 2, pp 1239-1246, Apr. 2011.

• M. Kezunovic, "Smart Fault Location for Smart Grids," IEEE Trans. Smart Grid, vol. 2, no. 1, pp 61-69, Mar. 2011.

• S. Lotfifard, M. Kezunovic and M. J. Mousavi,"A Systematic Approach for Ranking Distribution Systems Fault

Location Algorithms and Eliminating False Estimates," IEEE Trans. Power Del., vol. 28, no. 1, pp. 285-293, Jan. 2013.

• Y. Dong, C. Zheng, M. Kezunovic, "Enhancing Accuracy While Reducing Computation for Voltage-Sag Based

Distribution Fault Location," IEEE Trans. Power Delivery, vol. 28, no. 2, pp.1202-1212, Apr. 2013.

• P.-C. Chen, V. Malbasa, and M. Kezunovic, “Locating Sub-Cycle Faults in Distribution Network Applying Half-Cycle

DFT Method,” IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Apr. 2014.

• P.-C. Chen, Y. Dong, V. Malbasa, and M. Kezunovic, “Uncertainty of Measurement Error in Intelligent Electronic

Devices”, IEEE/PES General Meeting, Jul. 2014.

• P.-C. Chen, V. Malbasa, and M. Kezunovic, “Sensitivity Analysis of Voltage Sag Based Fault Location Algorithm,” in

Proceeding 18th Power Systems Computation Conference (PSCC), Aug. 2014.

• P.-C. Chen, et al., “Sensitivity of Voltage Sag Based Fault Location in Distribution Network to Sub-Cycle Faults”, in

Proceeding 46th North American Power Symposium (NAPS), Sep. 2014.

• P.-C. Chen, V. Malbasa, Y. Dong, and M. Kezunovic, “Sensitivity Analysis of Voltage Sag Based Fault Location with

Distributed Generation,” IEEE Trans. Smart Grid, Jan. 2015, in press.

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EPCC Workshop 2019 - 15 EPCC Workshop - May 12 – 15, 2019 - … · 2016. 11. 7. · ©2015 Mladen Kezunovic, All Rights Reserved Processing weather and power grid data using advanced