Fault Diagnosis and Prediction of Power Substation Equipment Maintenance Based on SVM

In recent years, with the rapid development of power system and the continuous upgrading of power equipment, power transformation equipment plays a crucial role in energy transmission and distribution. However, the fault of power transformation equipment inevitably affects the stable operation of power grid, so the maintenance, fault diagnosis, and prediction of power transformation equipment become particularly important. This paper discusses how to use support vector machine (SVM), a powerful machine learning algorithm, to solve the problem of power substation equipment. As a supervised learning method, support vector machines are known for their ability to classify and regression data in high-dimensional spaces. In the field of power equipment, we can use SVM to build fault diagnosis and prediction models, so as to achieve accurate evaluation of equipment state and prediction of future faults. First, this paper emphasizes the importance of timely maintenance and prediction. Then, we will introduce the principle and working mechanism of support vector machine in detail, and explain its advantages in processing complex data and high-dimensional features. Then, this paper will use the grid search method to optimize the support vector machine model, and then select evaluation indicators to evaluate the obtained model. The results show that the model can predict some types of faults can realize online monitoring of faults through real-time data, and avoid unplanned maintenance of equipment to a certain extent.