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Article

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

1
NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
2
NARI Technology Co., Ltd., Nanjing 211106, China
3
National Key Laboratory of Risk Defense Technology and Equipment for Power Grid Operation, Nanjing 211106, China
4
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
*
Authors to whom correspondence should be addressed.
Processes 2024, 12(1), 193; https://doi.org/10.3390/pr12010193
Submission received: 16 December 2023 / Revised: 9 January 2024 / Accepted: 12 January 2024 / Published: 16 January 2024
(This article belongs to the Section AI-Enabled Process Engineering)

Abstract

The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance.
Keywords: dissolved gas prediction; extreme learning machine; variational mode decomposition; marine predators algorithm; extended maximum correntropy criterion dissolved gas prediction; extreme learning machine; variational mode decomposition; marine predators algorithm; extended maximum correntropy criterion

Share and Cite

MDPI and ACS Style

Du, G.; Sheng, Z.; Liu, J.; Gao, Y.; Xin, C.; Ma, W. A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil. Processes 2024, 12, 193. https://doi.org/10.3390/pr12010193

AMA Style

Du G, Sheng Z, Liu J, Gao Y, Xin C, Ma W. A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil. Processes. 2024; 12(1):193. https://doi.org/10.3390/pr12010193

Chicago/Turabian Style

Du, Gang, Zhenming Sheng, Jiaguo Liu, Yiping Gao, Chunqing Xin, and Wentao Ma. 2024. "A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil" Processes 12, no. 1: 193. https://doi.org/10.3390/pr12010193

APA Style

Du, G., Sheng, Z., Liu, J., Gao, Y., Xin, C., & Ma, W. (2024). A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil. Processes, 12(1), 193. https://doi.org/10.3390/pr12010193

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