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A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold.
Wang, Yanwen; Chen, Peng; Zhao, Yongmei; Sun, Yanying.
Afiliación
  • Wang Y; School of Mechanical, Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
  • Chen P; School of Mechanical, Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
  • Zhao Y; CHN Energy Technology & Economics Research Institute Co., Ltd., Beijing 100083, China.
  • Sun Y; School of Mechanical, Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
Sensors (Basel) ; 22(23)2022 Dec 01.
Article en En | MEDLINE | ID: mdl-36502087
ABSTRACT
When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Señales Asistido por Computador Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Señales Asistido por Computador Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China