UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network
Partial discharge (PD) detection and localization is an important means for condition monitoring and diagnosis of power equipment. The existing time-difference based ultra-high frequency (UHF) PD localization techniques are limited in application due to their high costs. A novel PD localization meth...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2021-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202005016 |
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| author | Qichen YU Lingen LUO Fan WU Gehao SHENG Xiuchen JIANG |
| author_facet | Qichen YU Lingen LUO Fan WU Gehao SHENG Xiuchen JIANG |
| author_sort | Qichen YU |
| collection | DOAJ |
| description | Partial discharge (PD) detection and localization is an important means for condition monitoring and diagnosis of power equipment. The existing time-difference based ultra-high frequency (UHF) PD localization techniques are limited in application due to their high costs. A novel PD localization method is proposed based on generalized regression neural network (GRNN) and received signal strength indicator (RSSI) fingerprint, which consists of two stages. In the off-line stage of algorithm, a RSSI fingerprint map is built. In the on-line stage, the GRNN is used to calculate the position of the PD source. The field testing shows that the proposed UHF PD localization method has an average localization error of 0.51 m, and a cumulative probability of 81.6% for the localization error of less than 1 m. Compared to the minimum mean square error (MSE) of the Cramér-Rao lower bound (CRLB), which is based on RSSI log normal shadowing model positioning method, the cumulative probability of the GRNN localization error with the mean square error less than 0.6 m2 is 66.7%, which is better than CRLB. The proposed method overcomes the shortcomings of low positioning accuracy and high costs of the traditional methods, and has the characteristics of low hardware cost and good environmental adaptability. |
| format | Article |
| id | doaj-art-e8012ebbbd214f9d854b1e7a377b49c8 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-e8012ebbbd214f9d854b1e7a377b49c82025-08-20T02:05:04ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492021-02-01542111710.11930/j.issn.1004-9649.202005016zgdl-54-2-yuqichenUHF Partial Discharge Localization Methodology Based on Generalized Regression Neural NetworkQichen YU0Lingen LUO1Fan WU2Gehao SHENG3Xiuchen JIANG4Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaPartial discharge (PD) detection and localization is an important means for condition monitoring and diagnosis of power equipment. The existing time-difference based ultra-high frequency (UHF) PD localization techniques are limited in application due to their high costs. A novel PD localization method is proposed based on generalized regression neural network (GRNN) and received signal strength indicator (RSSI) fingerprint, which consists of two stages. In the off-line stage of algorithm, a RSSI fingerprint map is built. In the on-line stage, the GRNN is used to calculate the position of the PD source. The field testing shows that the proposed UHF PD localization method has an average localization error of 0.51 m, and a cumulative probability of 81.6% for the localization error of less than 1 m. Compared to the minimum mean square error (MSE) of the Cramér-Rao lower bound (CRLB), which is based on RSSI log normal shadowing model positioning method, the cumulative probability of the GRNN localization error with the mean square error less than 0.6 m2 is 66.7%, which is better than CRLB. The proposed method overcomes the shortcomings of low positioning accuracy and high costs of the traditional methods, and has the characteristics of low hardware cost and good environmental adaptability.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202005016partial dischargerssi fingerprintgrnnlog normal shadowing modelpositioning technology |
| spellingShingle | Qichen YU Lingen LUO Fan WU Gehao SHENG Xiuchen JIANG UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network Zhongguo dianli partial discharge rssi fingerprint grnn log normal shadowing model positioning technology |
| title | UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network |
| title_full | UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network |
| title_fullStr | UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network |
| title_full_unstemmed | UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network |
| title_short | UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network |
| title_sort | uhf partial discharge localization methodology based on generalized regression neural network |
| topic | partial discharge rssi fingerprint grnn log normal shadowing model positioning technology |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202005016 |
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