Contamination degree prediction of insulator surface based on exploratory factor analysis‐least square support vector machine combined model

Abstract

Abstract This study presents a combined model based on the exploratory factor analysis (EFA) and the least square support vector machine (LSSVM) to predict the contamination degree of insulator surface. Firstly, EFA method is utilised to reduce numerous influence factor variables of the insulator contamination into a few factor variables, which could decrease the complexity of the model. Then, regarding the above factor variables as new input variables, LSSVM model is established to predict the insulator contamination degree. In order to obtain the optimal predictive value, the non‐dominated sorting genetic algorithm II is applied on the optimization of LSSVM model parameters. The proposed EFA‐LSSVM combined model is compared with the models of LSSVM, back propagation neural network, and multiple linear regression on the model performance. Results indicate that the EFA‐LSSVM combined model in this study effectively overcomes the shortcomings of the other three models mentioned above in computational time, prediction accuracy and generalization ability. Finally, the feasibility of the proposed model in predicting contamination degree of insulator surface is verified by adopting the radar map of the evaluation indexes of model performance.

Keywords

• backpropagation
• genetic algorithms
• insulator contamination
• least squares approximations
• neural nets
• regression analysis