A High-Precision Error Calibration Technique for Current Transformers under the Influence of DC Bias

Sanlei Dang, Yong Xiao, Baoshuai Wang (), Dingqu Zhang, Bo Zhang, Shanshan Hu, Hongtian Song, Chi Xu and Yiqin Cai
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Sanlei Dang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Yong Xiao: Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
Baoshuai Wang: Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
Dingqu Zhang: Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
Bo Zhang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Shanshan Hu: Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
Hongtian Song: Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
Chi Xu: School of Electric Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yiqin Cai: School of Electric Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2023, vol. 16, issue 24, 1-19

Abstract: A bias current in the power system will cause saturation of the measuring current transformer (CT), leading to an increase in measurement error. Therefore, in this paper, we first conducted measurements of the direct current component in a 10 kV distribution system. Subsequently, a reverse extraction method for the CT distorted current under direct current bias conditions based on Random Forest Classification (RFC) and Long Short-Term Memory (LSTM) was proposed. This method involves two stages for the reverse extraction of CT distorted currents under direct current bias conditions. In the offline stage, data samples were generated by changing the operating environment of the CT. The RFC classification algorithm was used to divide the saturation levels of the CT, and for each sub-class, Particle Swarm Optimization–Long Short-Term Memory Network (PSO-LSTM) models were trained to establish the mapping relationship between the secondary distorted current and the primary current fundamental component. In the online stage, the saturated data segments were extracted from the secondary current waveform using wavelet transform, and these segments were input into the offline model for current reverse extraction. The simulation results show that the proposed method exhibited strong robustness under various CT conditions, and achieved high reconstruction accuracy for the primary current.

Keywords: current transformer; DC bias; saturation current reconstruction; PSO-LSTM; RFC (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: 2023
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