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Open AccessArticle

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

1
Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
2
College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China
3
NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
4
Wuhan NARI Limited Company of State Grid Electric Power Research Institute, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(2), 422; https://doi.org/10.3390/en13020422
Received: 29 November 2019 / Revised: 26 December 2019 / Accepted: 8 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Power Transformer Condition Assessment)
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. View Full-Text
Keywords: dissolved gas analysis; empirical mode decomposition; grey relation analysis; grey wolf optimizer; least squares support vector machine dissolved gas analysis; empirical mode decomposition; grey relation analysis; grey wolf optimizer; least squares support vector machine
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Zeng, B.; Guo, J.; Zhang, F.; Zhu, W.; Xiao, Z.; Huang, S.; Fan, P. 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. Energies 2020, 13, 422.

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