Fault Diagnosis and Asset Management of Power Transformer Using Adaptive Boost Machine Learning Algorithm. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Balaraman, Sujatha; Madavan, R.; Vedhanayaki, S.; Saroja, S.; Srinivasan, M.; Stonier, Albert Alexander
• Is Part Of: IOP conference series. Volume 1055:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Insulation Condition Monitoring -- Fault Diagnosis -- Dissolved Gas Analysis -- Machine Learning Algorithms -- ADABOOST Algorithm; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1055/1/012133
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Dissolved Gas Analysis (DGA) data of liquid insulation used to find the incipient faults such as partial discharge, thermal faults of various temperatures, discharge of high and low energy faults, combination of electrical and thermal faults in transformers. The conventional approaches of DGA namely Gas Ratio method, Duval triangle method and the Neural Network seems to be time consuming and sometimes yield erroneous results. In this paper, Adaptive BOOST machine learning algorithm is proposed, which is effective in classifying the transformer incipient faults. The results of proposed algorithm is compared with the results of different other machine learning algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Ensembler algorithm for the same set of transformers data. From the comparison, it is evident that ADABOOST machine learning algorithm performs well.