An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil
Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data cha...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/7/1687 |
| _version_ | 1797571605588606976 |
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| author | Fang Yuan Jiang Guo Zhihuai Xiao Bing Zeng Wenqiang Zhu Sixu Huang |
| author_facet | Fang Yuan Jiang Guo Zhihuai Xiao Bing Zeng Wenqiang Zhu Sixu Huang |
| author_sort | Fang Yuan |
| collection | DOAJ |
| description | Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models. |
| first_indexed | 2024-03-10T20:42:08Z |
| format | Article |
| id | doaj.art-92929f96e20b4f8e9e75c57fad699f7a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:42:08Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-92929f96e20b4f8e9e75c57fad699f7a2023-11-19T20:34:37ZengMDPI AGEnergies1996-10732020-04-01137168710.3390/en13071687An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer OilFang Yuan0Jiang Guo1Zhihuai Xiao2Bing Zeng3Wenqiang Zhu4Sixu Huang5Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaTransformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.https://www.mdpi.com/1996-1073/13/7/1687transformerdissolved gas content in oilforecastingchaos theoryphase space reconstructionweighted least-squares support vector machine |
| spellingShingle | Fang Yuan Jiang Guo Zhihuai Xiao Bing Zeng Wenqiang Zhu Sixu Huang An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil Energies transformer dissolved gas content in oil forecasting chaos theory phase space reconstruction weighted least-squares support vector machine |
| title | An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil |
| title_full | An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil |
| title_fullStr | An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil |
| title_full_unstemmed | An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil |
| title_short | An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil |
| title_sort | interval forecasting model based on phase space reconstruction and weighted least squares support vector machine for time series of dissolved gas content in transformer oil |
| topic | transformer dissolved gas content in oil forecasting chaos theory phase space reconstruction weighted least-squares support vector machine |
| url | https://www.mdpi.com/1996-1073/13/7/1687 |
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