Condition assessment for power transformer based on improved particle swarm optimization and Support Vector Machine

Power transformer is important to power system equipment. Due to the complex structure of power transformer, the running state of transform is difficult to be assessed accurately. The parameters of Support Vector Machine (SVM) have significant implications on the classification results. In order to obtain the best classification model, an improved particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of the support vector machine (SVM). The model is based on transformer dissolved gas analysis (DGA) technique as evaluation method, the running states of transformer are divided into excellent, good, normal, attention and fault five levels, where the fault level is divided into low-temperature failure of overheating, medium-temperature failure of overheating, high-temperature failure of overheating, low energy discharge, high energy discharge and partial discharge six categories. By the analysis of sample data, we prove that using the improved PSO algorithm to optimize the SVM classifier can increase the state assessment accuracy of transformer.