Fault Diagnosis of a Transformer using Fuzzy Model and PSO optimized SVM

In this paper a transformer fault diagnosis system based on a nature-based algorithm optimizing Support Vector Machine and Fuzzy Logic Model is proposed. Fault analysis and diagnosis are an integral part of operational reliability. Systems like SCADA collect data from various equipment in a power system network, however, cannot perform the critique fault diagnosis for the same. It, thereby, leads to additional costs. This paper uses the fuzzy model with a metaheuristic algorithm to build a predictive model for the data collected from various power transformers across Himachal Pradesh and IEC 10 database. A total of 225 datasets were collected and segregated into two sets. The datasets are created using the fuzzy model, IEC Ratio Method and concentration of key gases (ppm). Further, a support vector machine or SVM machine learning model is employed to classify the different faults in a transformer. The data is classified using binary and multiclass classification for an accurate diagnosis of transformer faults. The accuracy of SVM is improved by tuning its hyperparameters using Grid Search and Particle Swarm Optimization algorithm. A Classification Learner (MATLAB) model is also developed for the same dataset.