电子测量与仪器学报

邵凯旋,何怡刚,汪磊.基于多尺度熵分析与改进 SVM 的变压器故障识别[J].电子测量与仪器学报,2022,36(6):161-168

基于多尺度熵分析与改进 SVM 的变压器故障识别

Fault identification of transformer based on multiscaleentropy analysisand improved SVM

DOI：

中文关键词: 变压器故障识别多尺度标准差模糊熵支持向量机哈里斯鹰优化算法

英文关键词:transformer fault identification SDMFE HHO SVM

基金项目:国家重点研发计划“智能电网技术与装备”专项“电力物联网关键技术”项目（2020YFB0905905）、国家重点研发计划“重大科学仪器设备开发”项目（2016YFF0102200）、国家自然科学基金（51977153，51977161，51577046）、中央高校基本科研业务费专项资金（2042021kf0233）、国家自然科学基金重点项目（51637004）、装备预先研究重点项目（41402040301）、湖北省重点研发计划项目（2021BEA162）、武汉市局科技计划项目（20201G01）资助

作者	单位
邵凯旋	1.武汉大学电气与自动化学院
何怡刚	1.武汉大学电气与自动化学院
汪磊	1.武汉大学电气与自动化学院

Author	Institution
Shao Kaixuan	1.School of Electrical Engineering and Automation, Wuhan University
He Yigang	1.School of Electrical Engineering and Automation, Wuhan University
Wang Lei	1.School of Electrical Engineering and Automation, Wuhan University

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中文摘要:

为解决传统变压器故障识别方法提取故障特征难度大、识别准确率低等问题,提出基于多尺度标准差模糊熵( SDMFE) 和哈里斯鹰算法(HHO)优化支持向量机(SVM)的故障识别方法。首先,采用基于模糊熵的多尺度分析法量化变压器振动信号复杂的动态特性,提取多时间尺度下的故障特征。随后,将利用 SDMFE 获得的故障特征输入 SVM 分类器识别变压器不同的故障。同时,为了提升 SVM 的识别性能,引入 HHO 算法以自适应、准确地选择 SVM 参数。最后,利用变压器实测振动信号进行了对比试验。与不同的信息熵、不同的优化策略和不同的分类器相比,所提方法取得 98. 56%的最高识别准确度和最好的识别稳定性。结果表明所提方法能够有效提取故障敏感特征和准确识别变压器故障状态。

英文摘要:

To handle the problems of difficulty in extracting fault features and low identification accuracy of traditional transformer fault identification methods, a novel identification method is proposed by fusing standard deviation-based multiscale fuzzy entropy ( SDMFE) and Harris hawks algorithm ( HHO) optimized support vector machine ( SVM). Firstly, multiscale analysis method based on fuzzy entropy is employed to quantify the complex dynamic characteristics of transformer vibration signals, and then extract fault features under multi-time scales. Subsequently, the fault features obtained by SDMFE are input into SVM for identifying transformer different faults. At the same time, to improve SVM recognition performance, an optimization strategy integrating HHO algorithm is introduced to select SVM parameters adaptively and accurately. Finally, a comparative experiment is carried out using the measured vibration signal of the transformer. Compared with different information entropies, different optimization strategies and different classifiers, the proposed method achieves the highest identification accuracy of 98. 56% and identification stability. The results show that the proposed method can effectively extract fault sensitive features and accurately identify transformer fault status.

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