High-Dimensional Feature Optimization and Real-Time Prediction Model with Support Vector Machines for Fault Diagnosis of Electrical Equipment

The timely detection and treatment of electrical equipment fault events is a key problem that needs to be solved to ensure the normal and stable operation of power systems. In order to achieve accurate diagnosis and real-time prediction of electrical equipment fault problems, the study proposes an intelligent fault diagnosis model based on PCA and optimization parameter SVM and an electrical equipment fault prediction model based on lifting limit learning machine. The simulation and fault diagnosis examples show that: After using the ISFD-POPS electrical equipment fault diagnosis model proposed in this paper for experimental testing, the fault diagnosis accuracy is greatly improved, and the established prediction model basically meets the requirements of real-time prediction, with high accuracy and strong practicality.