电工技术学报  2015, Vol. 30 Issue (18): 213-222    DOI:
高电压与绝缘 |
云平台下并行总体经验模态分解局部放电信号去噪方法
宋亚奇1, 2, 周国亮1, 2, 朱永利1, 2, 李莉1, 2, 王德文1
1. 华北电力大学控制与计算机工程学院 保定 071003;
2. 华北电力大学新能源电力系统国家重点实验室 北京 102206
Research on Parallel Ensemble Empirical Mode Decomposition Denoising Method for Partial Discharge Signals Based on Cloud Platform
Song Yaqi1, 2, Zhou Guoliang1, 2, Zhu Yongli1, 2, Li Li1, 2, Wang Dewen1
1. North China Electric Power University Baoding 071003 China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China
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摘要 信号去噪是对输变电设备进行在线监测和诊断时首要解决的问题。鉴于总体经验模态分解(EEMD)方法对局部放电信号进行去噪的优势,设计了基于MapReduce模型的并行化EEMD算法(MR-EEMD),利用云平台提高算法的计算效率。在对分段包络线进行重构时,针对矩形窗的固有缺陷,提出了基于局部平稳度的自适应分段包络线重构算法(LF-ASER)进行分段边界的补偿处理,使重构的包络线误差减小到给定阈值范围内。实验结果表明MR-EEMD算法相对于EEMD性能提升显著,适合处理变压器的局部放电等高采样率信号,同时保持了EEMD去噪效果,并获得较高的可扩展性和加速比。
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关键词 局部放电信号去噪总体经验模态分解MapReduce包络线重构    
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 wordsPartial discharge    signal denoising    ensemble empirical mode decomposition    MapReduce    envelope reconstruction   
收稿日期: 2013-08-24      出版日期: 2015-10-20
PACS: TN911  
基金资助:国家自然科学基金(61074078),中央高校基本科研业务费专项资金(13MS88、13XS30),新能源电力系统国家重点实验室和河北省自然科学基金(F2014502069)资助项目
作者简介: 宋亚奇 男,1979年生,讲师,博士研究生,研究方向为电力信息智能处理、云计算。周国亮 男,1978年生,博士,副教授,研究方向为智能电网、大数据处理。
引用本文:   
宋亚奇, 周国亮, 朱永利, 李莉, 王德文. 云平台下并行总体经验模态分解局部放电信号去噪方法[J]. 电工技术学报, 2015, 30(18): 213-222. Song Yaqi, Zhou Guoliang, Zhu Yongli, Li Li, Wang Dewen. Research on Parallel Ensemble Empirical Mode Decomposition Denoising Method for Partial Discharge Signals Based on Cloud Platform. Transactions of China Electrotechnical Society, 2015, 30(18): 213-222.
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