A fault diagnosis method for active power factor correction power supply based on seagull algorithm optimized kernel-based extreme learning machine

被引:3
作者
Tang, Shengxue [1 ,2 ]
Wang, Hongfan [1 ,2 ]
Wang, Weiwei [1 ,2 ]
Liu, Chenglong [1 ,2 ]
机构
[1] Hebei Univ Technol, Sch Elect Engn, State Key Lab Reliabil & Intelligence Elect Equipm, Tianjin, Peoples R China
[2] Hebei Univ Technol, Sch Elect Engn, Key Lab Electromagnet Field & Elect Apparat Reliab, Tianjin, Peoples R China
关键词
APFC converter; fault classification; feature extraction; kernel-based extreme learning machine; time-frequency feature fusion; CONVERTER; PFC;
D O I
10.1002/cta.3821
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time-frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA-KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. To address the issue of diagnosing faults in active power factor correction (APFC) power supplies, this paper conducts an analysis of the waveform characteristics of power supply fault signals. Time-frequency fusion fault features of the APFC power supply in different fault modes are then extracted and constructed, followed by feature optimization. This paper proposes the SOA-KELM model of the APFC power supply, which utilizes feature vectors to classify faults, ultimately achieving hard and soft fault detection of the power supply.image
引用
收藏
页码:1116 / 1135
页数:20
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