A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids

Huimin Zhao, Lili He (), Yelun Peng, Zhikang Shuai, Zhixue Zhang and Liang Hu
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Huimin Zhao: CRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, China
Lili He: College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Yelun Peng: College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Zhikang Shuai: College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Zhixue Zhang: CRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, China
Liang Hu: CRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, China

Energies, 2024, vol. 17, issue 3, 1-19

Abstract: The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions.

Keywords: index terms microgrid; transient stability prediction; deep learning; time-varying spatial–temporal correlation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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