Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model
The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the t...
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MDPI AG
2018-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/1/225 |
| _version_ | 1817990208095780864 |
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| author | Jun Lin Gehao Sheng Yingjie Yan Jiejie Dai Xiuchen Jiang |
| author_facet | Jun Lin Gehao Sheng Yingjie Yan Jiejie Dai Xiuchen Jiang |
| author_sort | Jun Lin |
| collection | DOAJ |
| description | The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods. |
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| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T00:56:17Z |
| publishDate | 2018-01-01 |
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| series | Energies |
| spelling | doaj.art-6d21a954033f4dbb901cf59d35571f4c2022-12-22T02:21:36ZengMDPI AGEnergies1996-10732018-01-0111122510.3390/en11010225en11010225Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN ModelJun Lin0Gehao Sheng1Yingjie Yan2Jiejie Dai3Xiuchen Jiang4Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaDepartment of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaThe purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.http://www.mdpi.com/1996-1073/11/1/225dissolved gas in oilkernel principal component analysisfruit fly optimization algorithmgeneralized regression neural network |
| spellingShingle | Jun Lin Gehao Sheng Yingjie Yan Jiejie Dai Xiuchen Jiang Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model Energies dissolved gas in oil kernel principal component analysis fruit fly optimization algorithm generalized regression neural network |
| title | Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model |
| title_full | Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model |
| title_fullStr | Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model |
| title_full_unstemmed | Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model |
| title_short | Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model |
| title_sort | prediction of dissolved gas concentrations in transformer oil based on the kpca ffoa grnn model |
| topic | dissolved gas in oil kernel principal component analysis fruit fly optimization algorithm generalized regression neural network |
| url | http://www.mdpi.com/1996-1073/11/1/225 |
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