Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis. Issue 8 (5th March 2020)

• Record Type: Journal Article
• Authors: Illias, Hazlee Azil; Chan, Kai Choon; Mokhlis, Hazlie
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 8(2020)
• Page Start: 1575
• Page End: 1582
• Publication Date: 2020-03-05
• Subjects: regression analysis -- learning (artificial intelligence) -- neural nets -- support vector machines -- search problems -- chemical analysis -- power transformers -- power engineering computing -- transformer oil -- fault diagnosis -- power transformer insulation -- optimisation -- feature selection; automated transformer fault determination -- dissolved gas analysis -- artificial intelligence methods -- artificial neural network -- optimisation methods -- automated power transformer fault type detection -- hybrid feature selection–artificial intelligence–gravitational search algorithm technique -- hybrid feature selection–AI–GSA technique -- support vector machine -- stepwise regression; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.1189
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Dissolved gas analysis (DGA) is commonly used to identify the fault type in power transformers. However, the available DGA methods have certain limitations because every method depends on the concentration of the dissolved gases. Therefore, in this work, hybrid feature selection–artificial intelligence–gravitational search algorithm (GSA) techniques were proposed to determine the fault type of power transformers based on DGA data. The artificial intelligence (AI) methods applied include support vector machine and artificial neural network. Both AI methods were optimised by GSA to enhance the accuracy of the results. Feature selections using stepwise regression and robust regression were applied to utilise only significant gases. The accuracy of the results was tested with various ratios of testing and training data. Comparison of the results using the proposed method with other optimisation methods and the previous works was performed to validate the performance of the proposed technique. It was observed that the proposed hybrid feature selection–AI–GSA technique yields reasonable accuracy although fewer types of dissolved gases were used. Therefore, the proposed method can be recommended for the application of automated power transformer fault type detection based on DGA data in practice.