并行EEMD算法及其在局部放电信号特征提取中的应用

doi:10.19595/j.cnki.1000-6753.tces.170488

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Abstract

Waveform signals are common data in the condition monitoring of electrical power apparatuses, their processing becomes a data-intensive and computing-intensive problem in the background of big data. Ensemble empirical mode decomposition(EEMD) algorithm, which is adaptive, has advantages in analyzing nonlinear, non-stationary signals, butits application is limited by the high computational complexity. Based on the parallelismanalysis of EEMD, two different structure of parallel EEMD algorithm, namely epoch parallel and trial parallel, are designed and implemented on Spark platform. Epoch parallel EEMD, which segments waveform and processes fragments in parallel, is applicable to long waveform, but its results have some errors. Trial parallel EEMD, which parallelizes the empirical mode decomposition(EMD)trials, can get the same resultswith the original algorithm, but its memory requirement is relatively larger, so it is suitable for the waveform signals with little data volume.The proposed parallel EEMD algorithms are used to extract features fromPD waveform signals, and energy parameter and sample entropy of the IMFs are calculated asfeatures. Experimental results show that the features can be used to recognizetypes of partial discharge. The proposed parallel EEMD algorithms are more efficient than the existing EEMD, which saves the time of feature extraction process.

Key words： Ensemble empirical mode decomposition parallel computing Spark partial discharge wave signal processing

Received: 19 April 2017 Published: 13 June 2018

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TM76

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	Zhu Yongli
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Cite this article:

Zhu Yongli,Wang Liuwang. Parallel Ensemble Empirical Mode Decomposition and Its Application in Feature Extraction of Partial Discharge Signals[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2508-2519.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170488 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I11/2508

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