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

doi:10.11930/j.issn.1004-9649.202004162

Abstract

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.

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202004162

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

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