Integrating Learning and Physics based Computation for Fast Online Transient Analysis

Li, Jiaming; Zhao, Yue; Yue, Meng

doi:10.1109/isgt51731.2023.10066348

Title: Integrating Learning and Physics based Computation for Fast Online Transient Analysis

Journal Article · Wed Mar 22 00:00:00 EDT 2023 · Innovative Smart Grid Technologies (ISGT) (Online)

DOI:https://doi.org/10.1109/isgt51731.2023.10066348· OSTI ID:1984415

Li, Jiaming ^[1]; Zhao, Yue ^[1]; Yue, Meng ^[2]

Stony Brook Univ., NY (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)

In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.

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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; USDOE Office of Science (SC); National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: SC0012704; ECCS-2025152; N00014-22-1-2001

OSTI ID:: 1984415

Report Number(s):: BNL-224073-2023-JAAM

Journal Information:: Innovative Smart Grid Technologies (ISGT) (Online), Vol. 2023; Conference: 14. Conference on Innovative Smart Grid Technologies, North America (ISGT NA 2023), Washington, DC (United States), 16-19 Jan 2023; ISSN 2472-8152

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (8)

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Physics-Informed Neural Networks for Power Systems Misyris, George S.; Venzke, Andreas; Chatzivasileiadis, Spyros 2020 IEEE Power & Energy Society General Meeting (PESGM) https://doi.org/10.1109/PESGM41954.2020.9282004	conference	August 2020
Networked Time Series Shapelet Learning for Power System Transient Stability Assessment Zhu, Lipeng; Hill, David J. IEEE Transactions on Power Systems, Vol. 37, Issue 1 https://doi.org/10.1109/TPWRS.2021.3093423	journal	January 2022
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