A method for infrared image fault diagnosis of voltage heating equipment in intelligent substations is proposed, which combines multiple feature fusion methods with improved support vector machine (SVM), to address the issues of poor diagnostic accuracy and low efficiency in existing infrared image fault diagnosis methods for voltage heating equipment in intelligent substations. By using multiple feature fusion methods to fuse color features, edge features, and texture features, and optimizing SVM parameters (penalty factors and kernel parameters) through an improved imperial competition algorithm, and the fault diagnosis performance is improved. The feasibility of the proposed method is verified by comparing and analyzing numerical examples. The results show that the proposed method has high fault diagnosis performance in multiple voltage induced thermal equipment fault diagnosis, superior to the single feature fault diagnosis method, with a fault diagnosis accuracy rate of 94.83% and an average diagnosis time of 0.62 seconds. This has laid the foundation for the development of unmanned substations.
