Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

• Published in: PloS one 2018-01, Vol.13 (1), p.e0191366-e0191366
• Main Authors: Illias, Hazlee Azil, Zhao Liang, Wee
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biology and Life Sciences; Carbon dioxide; Cellulose; Classification; Computer and Information Sciences; Data reduction; Decomposition; Dissolved gases; Electric Power Supplies - statistics & numerical data; Electric power systems; Electrical engineering; Engineering and Technology; Equipment Failure - statistics & numerical data; Evolutionary algorithms; Fault detection; Fault diagnosis; Gas analysis; Gases; Gases - analysis; Identification; Maintenance; Maintenance costs; Methods; Mineral Oil - chemistry; Models, Statistical; Neural networks; Optimization; Particle swarm optimization; Physical Sciences; Power; Power Plants - statistics & numerical data; Regression Analysis; Research and Analysis Methods; Social Sciences; Support Vector Machine; Support vector machines; Swarm intelligence; Training; Transformers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0191366

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.