Research on Fault Diagnosis Technology for Electrical Equipment in Power Substations Based on Improved Support Vector Machine

In modern power systems, power substations serve as critical nodes for electric energy transmission and distribution, where the stable operation of electrical equipment is directly linked to the security and reliability of the entire power grid. However, electrical equipment is prone to various faults during long-term operation. Timely and accurate diagnosis of these faults is of great significance for minimizing power outage duration and reducing economic losses. Traditional fault diagnosis methods have numerous limitations when faced with complex and variable substation environments. In view of this, this paper proposes a fault diagnosis algorithm based on an improved Support Vector Machine (SVM). By introducing data preprocessing and parameter optimization techniques, the accuracy and efficiency of fault diagnosis are effectively enhanced. Experimental results show that compared with traditional methods, the proposed algorithm demonstrates significant advantages in both accuracy and operation time, providing a new approach for intelligent fault diagnosis of electrical equipment in power substations.