PSO and SLPSO to Improve the SVM with RBF Kernel for the Diagnosis of Power Transformer Oil by DGA

A Support Vector Machine (SVM) algorithm, combined with the Radial Basis Function (RBF), is employed in this paper for diagnosing power transformer oil through Dissolved Gas Analysis (DGA). The SVM algorithm's performance is enhanced by adjusting SVM parameter gamma “y” and the margin “C” for the RBF kernel. Such adjustments are achieved by coupling two optimization techniques; Particle Swarm Optimization (PSO) and Self-Learning Particle Swarm Optimizer (SLPSO). Compared to the conventional diagnosis techniques, improvements are obtained where an accuracy rate of 93.75% is achieved with the help of SLPSO optimizer.