Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing

This paper optimised the power grid equipment fault prediction model based on ML and high-performance computing, analysed the application of high-performance computers in online fault prediction and designed the overall structure of the mechanical equipment fault prediction and detection model. It explains the data classification and prediction in ML, describes how to establish prediction models and applies different ML algorithms to power grid equipment fault prediction models. Through experiments, comparing the optimisation effects of varying ML algorithms on power grid equipment fault prediction models, it was found that the Least Squares Support Vector Machine (LS-SVM) prediction algorithm has the highest accuracy and the best optimisation effect on power grid equipment fault prediction models. After using the LS-SVM prediction algorithm, the entire fault prediction time has been shortened.