Deep Koopman model predictive control for enhancing transient stability in power grids. (22nd June 2020)

• Record Type: Journal Article
• Authors: Ping, Zuowei; Yin, Zhun; Li, Xiuting; Liu, Yefeng; Yang, Tao
• Other Names: Wan Yan guestEditor.; Yang Tao guestEditor.; Yuan Ye guestEditor.; Lewis Frank L. guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 6(2021)
• Page Start: 1964
• Page End: 1978
• Publication Date: 2020-06-22
• Subjects: deep learning -- energy storage systems -- extended dynamic mode decomposition -- Koopman operator -- model predictive control -- transient stability; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5043
• Languages: English
• ISSNs: 1049-8923

Abstract

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.