A Fault-Severity-Assessment Model Based on Spatiotemporal Feature Fusion and Scene Generation for Optical Current Transformer

Haiwang Jin, Haiqing An, Zhendong Li, Zihao Tong, Haonan Dai, Fei Wang () and Mengke Xie
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Haiwang Jin: State Grid Jibei Electric Power Co., Ltd., EHV Power Transmission Company, Beijing 102488, China
Haiqing An: State Grid Jibei Electric Power Co., Ltd., EHV Power Transmission Company, Beijing 102488, China
Zhendong Li: State Grid Jibei Electric Power Co., Ltd., EHV Power Transmission Company, Beijing 102488, China
Zihao Tong: Department of Power Engineering, North China Electric Power University, Baoding 071003, China
Haonan Dai: Department of Power Engineering, North China Electric Power University, Baoding 071003, China
Fei Wang: Department of Power Engineering, North China Electric Power University, Baoding 071003, China
Mengke Xie: Beijing Joinbright Technology Co., Ltd., Beijing 100085, China

Energies, 2025, vol. 18, issue 6, 1-20

Abstract: Accurately identifying the fault type of an optical current transformer (optical CT) and evaluating the fault severity can provide strong support for the operation and maintenance of a direct current (DC) power system. In response to the problems that current research overlooks, the spatiotemporal features when making fault identification, which restrain the improvement of identification accuracy, and consider fault identification as an assessment of fault severity, which is unable to provide effective information for actual operation and maintenance work, this paper proposes an optical CT fault severity assessment model based on scene generation and spatiotemporal feature fusion. Firstly, a CNN-Transformer model is constructed to mine the fault characteristics in spatial and temporal dimensions by feature fusion, achieving accurate identification of fault types. Secondly, an improved synthetic minority oversampling method is adopted to generate virtual operating scenes, and the operating range under different operating states of the optical CT is statistically obtained. Finally, based on Shapley Additive Explanations (SHAP), the importance of the feature of optical CT is evaluated under different fault types. Reliant on the importance of features and operating range under different running states, the severity of the fault is assessed by quantifying the difference between the fault state and the normal state of the optical CT under the identified fault type. This study validated the effectiveness of the proposed method using actual operational data from an optical CT at a converter station in Hebei Province in China.

Keywords: optical current transformer; fault identification; fault severity assessment; spatiotemporal feature fusion; scene generation (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: 2025
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