Wavelet transform-based fault diagnosis method for power electronics

Yuwei Mu; Qianwei Shen

doi:10.1117/12.2679042

8 May 2023 Wavelet transform-based fault diagnosis method for power electronics

Yuwei Mu, Qianwei Shen

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350O (2023) https://doi.org/10.1117/12.2679042
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

Power electronic equipment’s general fault alarm, based on patrol vector, monitors for anomalies in the data thresholds. When abnormal is found, the alarm shall be timely reported, so as to timely screen the possible abnormalities of the power plant equipment, find out the problems and deal with them in advance. Because most fault cities of stations cause new vectors to deviate from their normal working space, the research in this paper mainly includes data theory, line parameter creation, data modeling, and fault alarm multiplication. First, the data is processed. The data is denoised by wavelet to remove the boundary noise in the data as much as possible and smooth the data. Then, the process-saving moment is established. The police model is constructed, and the process-saving moment is utilized. The experiment verifies that the method in this paper can put forward the included fault information and warn in advance before the fault. It gives the power plant equipment fault warning information, which can remind people to take measures in time to avoid personnel and economic losses.

Citation Download Citation

Yuwei Mu and Qianwei Shen "Wavelet transform-based fault diagnosis method for power electronics", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350O (8 May 2023); https://doi.org/10.1117/12.2679042

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