Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Xiaomeng Duan, Wei Cen (), Peidong He, Sixiang Zhao, Qi Li, Suan Xu, Ailing Geng and Yongxian Duan
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Xiaomeng Duan: China Electric Power Research Institute, Beijing 100000, China
Wei Cen: China Electric Power Research Institute, Beijing 100000, China
Peidong He: State Grid Sichuan Electric Power Limited Company Marketing Service Center, Chengdu 610000, China
Sixiang Zhao: Metrology Center, State Grid Jibei Electric Power Company Limited, Beijing 100000, China
Qi Li: School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Suan Xu: School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Ailing Geng: China Electric Power Research Institute, Beijing 100000, China
Yongxian Duan: China Electric Power Research Institute, Beijing 100000, China

Energies, 2024, vol. 17, issue 2, 1-18

Abstract: To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.

Keywords: DC energy quality; feature extraction; S transform; subtraction optimization algorithm; back propagation neural network (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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