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Title: A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outage numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.
Authors:
ORCiD logo [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Report Number(s):
BNL-114128-2017-JA
Journal ID: ISSN 1949-3053
Grant/Contract Number:
SC0012704
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Name: IEEE Transactions on Smart Grid; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
New York State Energy Research and Development Authority (NYSERDA); USDOE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION
OSTI Identifier:
1389221

Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., and Lofaro, Robert. A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data. United States: N. p., Web. doi:10.1109/TSG.2017.2704288.
Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., & Lofaro, Robert. A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data. United States. doi:10.1109/TSG.2017.2704288.
Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., and Lofaro, Robert. 2017. "A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data". United States. doi:10.1109/TSG.2017.2704288. https://www.osti.gov/servlets/purl/1389221.
@article{osti_1389221,
title = {A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data},
author = {Yue, Meng and Toto, Tami and Jensen, Michael P. and Giangrande, Scott E. and Lofaro, Robert},
abstractNote = {Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outage numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.},
doi = {10.1109/TSG.2017.2704288},
journal = {IEEE Transactions on Smart Grid},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {5}
}