Bearing temperature prediction of hydroelectric unit based on PSO-SVR

Abstract

The prediction of bearing temperature is of significant importance for optimizing the operation and ensuring the stability of hydroelectric units. Based on practical operational experience, we establish a correlated mapping of bearing temperature during the operation of hydroelectric units and the main factors influencing its variations. We introduce a Support Vector Regression (SVR) model and employ the Particle Swarm Optimization (PSO) algorithm to optimize the penalty coefficient and insensitive loss coefficient of the SVR model. This leads to the development of a PSO-SVRbased bearing temperature prediction model for hydroelectric units. We compare the prediction accuracy of this model with other models such as BP neural networks and SVR. The results indicate that the proposed method yields predictions that are closer to the actual values, effectively achieving intelligent prediction of bearing temperature for hydroelectric units.