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Deep Koopman model predictive control for enhancing transient stability in power grids
Author(s) -
Ping Zuowei,
Yin Zhun,
Li Xiuting,
Liu Yefeng,
Yang Tao
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5043
Subject(s) - transient (computer programming) , control theory (sociology) , stability (learning theory) , artificial neural network , nonlinear system , model predictive control , computer science , electric power system , operator (biology) , curse of dimensionality , power (physics) , generator (circuit theory) , control engineering , control (management) , engineering , artificial intelligence , machine learning , biochemistry , physics , chemistry , repressor , quantum mechanics , transcription factor , gene , operating system
Summary In this article, we propose a deep Koopman model predictive control (MPC) strategy to improve the transient stability of power grids in a fully data‐driven manner. Due to the high‐dimensionality and the nonlinearity of the transient process, we use the Koopman operator to map the original nonlinear dynamics into an infinite dimensional linear system. To facilitate the control design, we first utilize the deep neural network method to efficiently train observable functions to approximate the Koopman operator so that the obtained dynamics in the high dimensional space is a linear system. We then propose an MPC strategy for the obtained high dimensional linear system. The proposed control strategy utilizes energy storage units, which inject or absorb real power at the synchronous generator buses to enhance the transient stability. Simulation studies implemented on the IEEE 9‐bus 3‐machine test system and the IEEE 39‐bus 10‐machine test system illustrate the performance of the proposed deep Koopman MPC strategy. The results demonstrate that the proposed control strategy effectively enhances the transient stability of the system even in the presence of severe faults.