Advanced SVM and KNN Algorithms for Fault Detection in Power Transformers based on Dissolved Gas Analysis

Power transformers, as oil-immersed equipment, are primarily diagnosed using dissolved gas analysis (DGA), a widely recognized and effective fault detection method. Over time, numerous approaches have been developed to identify transformer faults through DGA, with many incorporating artificial intelligence to enhance diagnostic accuracy. In this context, this study enhances the support vector machine and k-nearest neighbors algorithms by integrating them into the iterative processes of the artificial bee colony and crow search algorithms for fault identification. Chaos theory was embedded into the optimization framework to improve convergence and avoid local optima. Nine input vectors, derived from a dataset of 501 samples covering six fault types, were used to evaluate the enhanced algorithms. An improved binary crow search algorithm was employed to optimize the input vectors, effectively identifying the key features for fault diagnosis. The proposed enhancement achieved an accuracy exceeding 94%, demonstrating the potential of refined methodologies for practical transformer diagnostics.