On Learning-Based Model for Dynamic Granular Prediction of Power Outages Under Extreme Events

Zhao, Tianqiao; Satoshi, Endo; Yue, Meng; Jensen, Michael; Marschilok, Amy; Nugent, Brian; Cerruti, Brian; Spanos, Constantine

doi:10.1109/isgt-la56058.2023.10328277

On Learning-Based Model for Dynamic Granular Prediction of Power Outages Under Extreme Events

Conference · 06 November 2023

DOI:https://doi.org/10.1109/isgt-la56058.2023.10328277· OSTI ID:2440516

Zhao, Tianqiao; Satoshi, Endo; Yue, Meng; Jensen, Michael; Marschilok, Amy; Nugent, Brian; Cerruti, Brian; Spanos, Constantine

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research

DOE Contract Number:: SC0012704

OSTI ID:: 2440516

Report Number(s):: BNL-226083-2024-COPA

Country of Publication:: United States

Language:: English

References (14)

A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation Xia, Min; Shao, Haidong; Ma, Xiandong IEEE Transactions on Industrial Informatics, Vol. 17, Issue 10 https://doi.org/10.1109/TII.2021.3056867	journal	October 2021
A Bayesian Approach-Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data Yue, Meng; Toto, Tami; Jensen, Michael P. IEEE Transactions on Smart Grid, Vol. 9, Issue 6 https://doi.org/10.1109/TSG.2017.2704288	journal	November 2018
A Bayesian Network Model for Predicting Outages of Distribution System Caused by Hurricanes Li, Boda; Chen, Ying; Huang, Shaowei 2020 IEEE Power & Energy Society General Meeting (PESGM) https://doi.org/10.1109/PESGM41954.2020.9281923	conference	August 2020
Machine Learning Based Power Grid Outage Prediction in Response to Extreme Events Eskandarpour, Rozhin; Khodaei, Amin IEEE Transactions on Power Systems, Vol. 32, Issue 4 https://doi.org/10.1109/TPWRS.2016.2631895	journal	July 2017
A Methodology for Evaluation of Hurricane Impact on Composite Power System Reliability Liu, Yong; Singh, Chanan IEEE Transactions on Power Systems, Vol. 26, Issue 1 https://doi.org/10.1109/TPWRS.2010.2050219	journal	February 2011
Risk analysis and management in power outage and restoration: A literature survey Castillo, Anya Electric Power Systems Research, Vol. 107 https://doi.org/10.1016/j.epsr.2013.09.002	journal	February 2014
Outage Cause Detection in Power Distribution Systems Based on Data Mining Bashkari, Mohammad Sadegh; Sami, Ashkan; Rastegar, Mohammad IEEE Transactions on Industrial Informatics, Vol. 17, Issue 1 https://doi.org/10.1109/TII.2020.2966505	journal	January 2021
Data-Driven Classifier for Extreme Outage Prediction Based On Bayes Decision Theory Mohammadian, Mostafa; Aminifar, Farrokh; Amjady, Nima IEEE Transactions on Power Systems, Vol. 36, Issue 6 https://doi.org/10.1109/TPWRS.2021.3086031	journal	November 2021
Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes: Comparison and Validation of Statistical Methods Nateghi, Roshanak; Guikema, Seth D.; Quiring, Steven M. Risk Analysis, Vol. 31, Issue 12 https://doi.org/10.1111/j.1539-6924.2011.01618.x	journal	April 2011
Generating multivariate load states using a conditional variational autoencoder Wang, Chenguang; Sharifnia, Ensieh; Gao, Zhi Electric Power Systems Research, Vol. 213 https://doi.org/10.1016/j.epsr.2022.108603	journal	December 2022
Dynamic Modeling of Power Outages Caused by Thunderstorms Alpay, Berk A.; Wanik, David; Watson, Peter Forecasting, Vol. 2, Issue 2 https://doi.org/10.3390/forecast2020008	journal	May 2020
Predicting Hurricane Power Outages to Support Storm Response Planning Guikema, Seth David; Nateghi, Roshanak; Quiring, Steven M. IEEE Access, Vol. 2 https://doi.org/10.1109/ACCESS.2014.2365716	journal	January 2014
Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data Guikema, S. D.; Quiring, S. M. Reliability Engineering & System Safety, Vol. 99 https://doi.org/10.1016/j.ress.2011.10.012	journal	March 2012
Learning long-term dependencies with gradient descent is difficult Bengio, Y.; Simard, P.; Frasconi, P. IEEE Transactions on Neural Networks, Vol. 5, Issue 2 https://doi.org/10.1109/72.279181	journal	March 1994

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