Partial Discharge Data Matching Method for GIS Case-Based Reasoning
With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/19/3677 |
| _version_ | 1818016054786392064 |
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| author | Jiejie Dai Yingbing Teng Zhaoqi Zhang Zhongmin Yu Gehao Sheng Xiuchen Jiang |
| author_facet | Jiejie Dai Yingbing Teng Zhaoqi Zhang Zhongmin Yu Gehao Sheng Xiuchen Jiang |
| author_sort | Jiejie Dai |
| collection | DOAJ |
| description | With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods. |
| first_indexed | 2024-04-14T07:06:46Z |
| format | Article |
| id | doaj.art-2b365f5a0e7d4aee8a54f53f800c471c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T07:06:46Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2b365f5a0e7d4aee8a54f53f800c471c2022-12-22T02:06:33ZengMDPI AGEnergies1996-10732019-09-011219367710.3390/en12193677en12193677Partial Discharge Data Matching Method for GIS Case-Based ReasoningJiejie Dai0Yingbing Teng1Zhaoqi Zhang2Zhongmin Yu3Gehao Sheng4Xiuchen Jiang5State Grid Shanghai Shinan Electric Power Supply Company, Xinbei Road No.268, Shanghai 201199, ChinaState Grid Shanghai Shinan Electric Power Supply Company, Xinbei Road No.268, Shanghai 201199, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Dongchuan Road No.800, Shanghai 200240, ChinaState Grid Shanghai Electric Power Company, Yuanshen Road No.1122, Shanghai 200120, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Dongchuan Road No.800, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Dongchuan Road No.800, Shanghai 200240, ChinaWith the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.https://www.mdpi.com/1996-1073/12/19/3677partial dischargegas insulated switchgearcase-based reasoningdata matchingvariational autoencoder |
| spellingShingle | Jiejie Dai Yingbing Teng Zhaoqi Zhang Zhongmin Yu Gehao Sheng Xiuchen Jiang Partial Discharge Data Matching Method for GIS Case-Based Reasoning Energies partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder |
| title | Partial Discharge Data Matching Method for GIS Case-Based Reasoning |
| title_full | Partial Discharge Data Matching Method for GIS Case-Based Reasoning |
| title_fullStr | Partial Discharge Data Matching Method for GIS Case-Based Reasoning |
| title_full_unstemmed | Partial Discharge Data Matching Method for GIS Case-Based Reasoning |
| title_short | Partial Discharge Data Matching Method for GIS Case-Based Reasoning |
| title_sort | partial discharge data matching method for gis case based reasoning |
| topic | partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder |
| url | https://www.mdpi.com/1996-1073/12/19/3677 |
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