云平台下并行总体经验模态分解局部放电信号去噪方法

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Abstract Signal denoising is the primary issue when conducting online monitoring and diagnosing of electric transmission and transformation equipments. In view of the advantage of ensemble empirical mode decomposition (EEMD) for partial discharge signal denoising, the parallel EEMD algorithm based on Map Reduce model, named MR-EEMD, is designed to improve the computational efficiency by taking advantage of the cloud platform. In consideration of the inherent defects of the rectangular window, the local flatness-adaptive segmentation envelope reconstruction algorithm (LF-ASER) is proposed to compensate segmented boundary so that the envelope error can be reduced to a given threshold range. The experimental results show that MR-EEMD can be executed much faster than EEMD for the transformer partial discharge high sampling rate signal and maintains good denoising results, high scalability, and speedup.

Key words： Partial discharge signal denoising ensemble empirical mode decomposition MapReduce envelope reconstruction

Received: 24 August 2013 Published: 20 October 2015

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TN911

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	Articles by authors
	Song Yaqi
	Zhou Guoliang
	Zhu Yongli
	Li Li
	Wang Dewen

Cite this article:

Song Yaqi,Zhou Guoliang,Zhu Yongli等. Research on Parallel Ensemble Empirical Mode Decomposition Denoising Method for Partial Discharge Signals Based on Cloud Platform[J]. Transactions of China Electrotechnical Society, 2015, 30(18): 213-222.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I18/213

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