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Open AccessArticle

Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation

1
Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, Guangxi, China
2
State Grid Henan Electric Power Research Institute, Zhengzhou 450052, Henan, China
3
National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, Guangxi, China
4
Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA
*
Author to whom correspondence should be addressed.
Polymers 2019, 11(1), 85; https://doi.org/10.3390/polym11010085
Received: 24 December 2018 / Accepted: 28 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Polymers for Energy Applications)
A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods. View Full-Text
Keywords: transformer polymer insulation; dissolved gases; wavelet technique; imperialist competition algorithm; least squares support vector machine transformer polymer insulation; dissolved gases; wavelet technique; imperialist competition algorithm; least squares support vector machine
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Liu, J.; Zheng, H.; Zhang, Y.; Li, X.; Fang, J.; Liu, Y.; Liao, C.; Li, Y.; Zhao, J. Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation. Polymers 2019, 11, 85.

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