A Grounding Current Prediction Method Based on Frequency-Enhanced Transformer

Na Zhang, Gang Yang (), Zilong Fu and Junsheng Hou
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Na Zhang: Electric Power Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Gang Yang: Electric Power Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Zilong Fu: Substation Operation and Maintenance Center, State Grid Shanxi Electric Power Company Jincheng Power Supply Company, Jincheng 048000, China
Junsheng Hou: Substation Operation and Maintenance Center, State Grid Shanxi Electric Power Company Jincheng Power Supply Company, Jincheng 048000, China

Energies, 2024, vol. 18, issue 1, 1-23

Abstract: Concerning the problem that the coupling relationship in substation scenarios is complex and the Transformer model makes it difficult to capture the correlation between multiple variables of grounding current, resulting in low accuracy of grounding current prediction, a ground current prediction method based on frequency-enhanced Transformer is proposed. Firstly, in the data preprocessing stage, the best frequency domain decomposition algorithm is designed to obtain the high-frequency and low-frequency component data containing different component features so as to enhance the initial features that the model focuses on. Secondly, the data slicing and embedding module is designed to replace the original embedding module of the Transformer to realize the enhanced extraction of local features of the data. Finally, in the feature extraction stage, an enhanced attention mechanism is introduced to replace the standard attention mechanism to capture the intrinsic features of the sequence time dimension and the variable dimension in parallel so as to improve the extraction ability of Transformer multivariate features. Experimental results on the self-built grounding current dataset and the public dataset show that the proposed method outperforms existing advanced methods, verifying the effectiveness of the proposed method.

Keywords: time series; attention mechanisms; grounding current prediction; transformer (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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