基于反卷积波束形成算法的干式变压器异响故障识别技术

doi:10.11930/j.issn.1004-9649.202004162

中国电力 ›› 2022, Vol. 55 ›› Issue (2): 90-97.DOI: 10.11930/j.issn.1004-9649.202004162

基于反卷积波束形成算法的干式变压器异响故障识别技术

包海龙¹, 邵宇鹰¹, 王枭², 彭鹏¹, 袁国刚², 庄贝妮¹

1. 国网上海市电力公司，上海 200122;
2. 上海睿深电子科技有限公司，上海 201108

收稿日期:2020-04-01 修回日期:2020-10-19 出版日期:2022-02-28 发布日期:2022-02-23
作者简介:包海龙（1974—），男，硕士研究生，从事智能电网技术研究与应用，E-mail：sepdi@163.com;邵宇鹰（1977—），男，通信作者，博士研究生，从事电力设备状态监测和新能源技术研究与应用，E-mail：yyshao@163.com
基金资助:
国网上海市电力公司科技项目（52097018000F）

Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer

BAO Hailong¹, SHAO Yuying¹, WANG Xiao², PENG Peng¹, YUAN Guogang², ZHUANG Beini¹

1. State Grid Shanghai Electric Power Company, Shanghai 200122 China;
2. Shanghai Rhythm Electronic Technology Co., Ltd., Shanghai 201108 China

Received:2020-04-01 Revised:2020-10-19 Online:2022-02-28 Published:2022-02-23
Supported by:
This work is supported by Science and Technology Project of State Grid Shanghai Electric Power Compang (No. 52097018000F)

摘要/Abstract

摘要： 针对常规波束形成算法定位精度不高的问题，将基于反卷积变换的波束形成算法应用于干式变压器异响故障识别，分析了反卷积变换的波束形成算法基本原理及其用于干式变压器异响故障识别的可行性；研究了一种采用异响精准定位联合声纹图谱特征识别的干式变压器异响故障识别方法；提出了“高频特征峰能量比”的概念，用于量化机械异响严重程度；最后通过实验测试和现场验证的方法，证明方法的有效性和准确性。

关键词: 反卷积, 波束形成算法, 干式变压器, 异响故障识别

Abstract: To improve the accuracy of the conventional beamforming location algorithm, the deconvolution beamforming algorithm is proposed for the abnormal noise fault identification of dry-type transformer. The basic principle of deconvolution beamforming algorithm is analyzed, and its applicability to the dry-type transformer abnormal-noise fault identification is verified. A dry-type transformer fault identification method based on the accurate location of abnormal-noise is studied, where the feature recognition of voice print is considered. The concept of "the energy ratio of high-frequency characteristic peak" is firstly proposed to quantify the severity of mechanical abnormal noise. Finally, experimental test and field verification validate the effectiveness and accuracy of the proposed method.

Key words: deconvolution transform, beamforming algorithm, dry-type transformer, fault identification.

包海龙, 邵宇鹰, 王枭, 彭鹏, 袁国刚, 庄贝妮. 基于反卷积波束形成算法的干式变压器异响故障识别技术[J]. 中国电力, 2022, 55(2): 90-97.

BAO Hailong, SHAO Yuying, WANG Xiao, PENG Peng, YUAN Guogang, ZHUANG Beini. Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer[J]. Electric Power, 2022, 55(2): 90-97.

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https://www.electricpower.com.cn/CN/Y2022/V55/I2/90

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基于反卷积波束形成算法的干式变压器异响故障识别技术

Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer

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