Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Bing Zeng, Jiang Guo, Fangqing Zhang, Wenqiang Zhu, Zhihuai Xiao, Sixu Huang and Peng Fan
Additional contact information
Bing Zeng: Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
Jiang Guo: Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
Fangqing Zhang: Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
Wenqiang Zhu: Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
Zhihuai Xiao: College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China
Sixu Huang: Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
Peng Fan: NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China

Energies, 2020, vol. 13, issue 2, 1-20

Abstract: Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Keywords: dissolved gas analysis; empirical mode decomposition; grey relation analysis; grey wolf optimizer; least squares support vector machine (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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