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©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
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©2015 Mladen Kezunovic, All Rights Reserved
• What: The problem
• Why: The background
• How: The data and analytics
• When: The spatial/temporal
focus
• Applications (Examples)
Outline
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©2015 Mladen Kezunovic, All Rights Reserved
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).
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©2015 Mladen Kezunovic, All Rights Reserved
What: The problem
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©2015 Mladen Kezunovic, All Rights Reserved
• What: The problem
• Why: The background
• How: The data and analytics
• When: The spatial/temporal
focus
• Applications (Examples)
Outline
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©2015 Mladen Kezunovic, All Rights Reserved
• 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
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©2015 Mladen Kezunovic, All Rights Reserved
Why: The background
Three directions
• Data integration
• Risk assessment
• Time/space (GIS)
prediction
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©2015 Mladen Kezunovic, All Rights Reserved
• What: The problem
• Why: The background
• How: The data and analytics
• When: The spatial/temporal
focus
• Applications (Examples)
Outline
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
• What: The problem
• Why: The background
• How: The data and analytics
• When: The spatial/temporal
focus
• Applications (Examples)
Outline
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©2015 Mladen Kezunovic, All Rights Reserved
Spatial/temporal state of risk
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©2015 Mladen Kezunovic, All Rights Reserved
Risk for the grid
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©2015 Mladen Kezunovic, All Rights Reserved
• What: The problem
• Why: The background
• How: The data and analytics
• When: The spatial/temporal
focus
• Applications (Examples)
Outline
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
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.
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved 18
Insulation coordination
Results
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©2015 Mladen Kezunovic, All Rights Reserved
Insulation Coordination
©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
Distribution outage prediction
Overhead distribution network Wind data of southeast region of USA
Global vegetation
data
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©2015 Mladen Kezunovic, All Rights Reserved
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
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©2015 Mladen Kezunovic, All Rights Reserved
• 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
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©2015 Mladen Kezunovic, All Rights Reserved
• 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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