Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Abstract

The accurate diagnosis of transmission line fault types is a prerequisite for quickly removing faults and restoring power supply, as well as the key to effectively reducing user economic losses, ensuring stable operation of the power system. The rapid development of artificial intelligence technology has been a promising way for fault diagnosis. However, the existing methods are still limited by small simples and accuracy of generalization. To overcome these problems, a transmission line fault diagnosis method based on an improved multiple SVM (MSVM) model is proposed in this paper. Firstly, the transmission line was selected as the research object, and its fault types and causes were analyzed in detail. Then, typical fault information are selected and corresponding MSVM model is established. Meanwhile, genetic algorithm (GA) is adopted to optimize model parameters to improve the accuracy of generalization. Finally, an improved IEEE-30 node test system and a real-world testing data are adopted to verify the accuracy and feasibility of the proposed method. Through analysis, fault diagnosis accuracy of the proposed method can be improved by up to 11% with better fitness value.