Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer
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 dr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004162 |
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| _version_ | 1850069268599668736 |
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| author | Hailong BAO Yuying SHAO Xiao WANG Peng PENG Guogang YUAN ZHUANG Beini |
| author_facet | Hailong BAO Yuying SHAO Xiao WANG Peng PENG Guogang YUAN ZHUANG Beini |
| author_sort | Hailong BAO |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-87bc6f96ce3e43fdb74be8abbcd2364d |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-87bc6f96ce3e43fdb74be8abbcd2364d2025-08-20T02:47:49ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-02-01552909710.11930/j.issn.1004-9649.202004162zgdl-55-2-baohailongDeconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type TransformerHailong BAO0Yuying SHAO1Xiao WANG2Peng PENG3Guogang YUAN4ZHUANG Beini5State Grid Shanghai Electric Power Company, Shanghai 200122 ChinaState Grid Shanghai Electric Power Company, Shanghai 200122 ChinaShanghai Rhythm Electronic Technology Co., Ltd., Shanghai 201108 ChinaState Grid Shanghai Electric Power Company, Shanghai 200122 ChinaShanghai Rhythm Electronic Technology Co., Ltd., Shanghai 201108 ChinaState Grid Shanghai Electric Power Company, Shanghai 200122 ChinaTo 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004162deconvolution transformbeamforming algorithmdry-type transformerfault identification. |
| spellingShingle | Hailong BAO Yuying SHAO Xiao WANG Peng PENG Guogang YUAN ZHUANG Beini Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer Zhongguo dianli deconvolution transform beamforming algorithm dry-type transformer fault identification. |
| title | Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer |
| title_full | Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer |
| title_fullStr | Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer |
| title_full_unstemmed | Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer |
| title_short | Deconvolution Beamforming Algorithm Based Abnormal Noise Fault Identification of Dry-Type Transformer |
| title_sort | deconvolution beamforming algorithm based abnormal noise fault identification of dry type transformer |
| topic | deconvolution transform beamforming algorithm dry-type transformer fault identification. |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004162 |
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