Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines
With the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. To overcome the computational complexit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/10/2535 |
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| _version_ | 1849710858285875200 |
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| author | Xiaoming Yu Jun Wang Ke Zhang Zhijun Chen Ming Tong Sibo Sun Jiapeng Shen Li Zhang Chuyang Wang |
| author_facet | Xiaoming Yu Jun Wang Ke Zhang Zhijun Chen Ming Tong Sibo Sun Jiapeng Shen Li Zhang Chuyang Wang |
| author_sort | Xiaoming Yu |
| collection | DOAJ |
| description | With the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. To overcome the computational complexity, poor real-time performance, and limited generalization of existing methods like GRU-GAN and SOM-LSTM, this study proposes a hybrid framework combining Bayesian multiple imputation with a Support Vector Machine (SVM) for data repair. The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. A kernel-based SVM with RBF and soft margin optimization is then applied for nonlinear calibration to enhance robustness and consistency in high-dimensional scenarios. Experimental validation focusing on capacitor voltage, current, and temperature parameters of UPFC submodules under a 50% missing data scenario demonstrates that the proposed method achieves an 18.7% average error reduction and approximately 30% computational efficiency improvement compared to single imputation and traditional multiple imputation approaches, significantly outperforming neural network models. This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. Future research will explore dynamic weight optimization and extend the framework to multi-source heterogeneous data applications. |
| format | Article |
| id | doaj-art-c302db00f7c246e4be861efd0d39677a |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c302db00f7c246e4be861efd0d39677a2025-08-20T03:14:46ZengMDPI AGEnergies1996-10732025-05-011810253510.3390/en18102535Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector MachinesXiaoming Yu0Jun Wang1Ke Zhang2Zhijun Chen3Ming Tong4Sibo Sun5Jiapeng Shen6Li Zhang7Chuyang Wang8State Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211106, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211106, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211106, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211106, ChinaWith the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. To overcome the computational complexity, poor real-time performance, and limited generalization of existing methods like GRU-GAN and SOM-LSTM, this study proposes a hybrid framework combining Bayesian multiple imputation with a Support Vector Machine (SVM) for data repair. The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. A kernel-based SVM with RBF and soft margin optimization is then applied for nonlinear calibration to enhance robustness and consistency in high-dimensional scenarios. Experimental validation focusing on capacitor voltage, current, and temperature parameters of UPFC submodules under a 50% missing data scenario demonstrates that the proposed method achieves an 18.7% average error reduction and approximately 30% computational efficiency improvement compared to single imputation and traditional multiple imputation approaches, significantly outperforming neural network models. This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. Future research will explore dynamic weight optimization and extend the framework to multi-source heterogeneous data applications.https://www.mdpi.com/1996-1073/18/10/2535unified power flow controller (UPFC)Bayesian multiple imputationsupport vector machinedata restoration |
| spellingShingle | Xiaoming Yu Jun Wang Ke Zhang Zhijun Chen Ming Tong Sibo Sun Jiapeng Shen Li Zhang Chuyang Wang Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines Energies unified power flow controller (UPFC) Bayesian multiple imputation support vector machine data restoration |
| title | Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines |
| title_full | Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines |
| title_fullStr | Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines |
| title_full_unstemmed | Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines |
| title_short | Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines |
| title_sort | research on missing data estimation method for upfc submodules based on bayesian multiple imputation and support vector machines |
| topic | unified power flow controller (UPFC) Bayesian multiple imputation support vector machine data restoration |
| url | https://www.mdpi.com/1996-1073/18/10/2535 |
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