Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines

Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen (), Li Zhang and Chuyang Wang
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Xiaoming Yu: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Jun Wang: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Ke Zhang: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Zhijun Chen: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Ming Tong: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Sibo Sun: College of Electrical and Power Engineering, Hohai University, Nanjing 211106, China
Jiapeng Shen: College of Electrical and Power Engineering, Hohai University, Nanjing 211106, China
Li Zhang: College of Electrical and Power Engineering, Hohai University, Nanjing 211106, China
Chuyang Wang: College of Electrical and Power Engineering, Hohai University, Nanjing 211106, China

Energies, 2025, vol. 18, issue 10, 1-22

Abstract: 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.

Keywords: unified power flow controller (UPFC); Bayesian multiple imputation; support vector machine; data restoration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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