Integrating Learning and Physics based Computation for Fast Online Transient Analysis
Integrating Learning and Physics based Computation for Fast Online Transient Analysis
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DOI：
10.1109/isgt51731.2023.10066348
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发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
Jiaming Li;Yue Zhao;M. Yue
中科院分区：
文献类型：
--
作者：
Jiaming Li;Yue Zhao;M. Yue
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.
