Transformer Fault Diagnosis Based on Feature Selection and ICOA-LSSVM

In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on feature selection and improved chimp optimization algorithm(ICOA) optimized least squares support vector machine(LSSVM) is proposed. Two methods, F-score and information gain, are used to screen the fault features, and the input of transformer fault diagnosis model is determined according to the feature selection results. Using ICOA algorithm to optimize the penalty factor and kernel parameters of LSSVM, a transformer fault diagnosis model based on feature selection and ICOA-LSSVM is established. The actual transformer fault data are used for example analysis, and compared with other transformer fault diagnosis methods. The results show that the accuracy of the diagnosis results of ICOA-LSSVM model considering feature selection is as high as 95.83%, higher than other methods, which verifies the correctness and superiority of the transformer fault diagnosis method proposed.