A Low-voltage Switch Electrical Lifetime Prediction Method Based on RVM Joint SVR
The electrical lifetime of low-voltage switches such as relays is an important indicator of their reliability,and accurate prediction of the electrical lifetime is crucial for the safe and stable operation of the entire power grid system.The traditional neural network-based method for predicting electrical lifetime has low accuracy and weak generalization ability,which limits its practical application.To address this issue,a combined model based on relevance vector machine(RVM)and support vector regression(SVR)is proposed to achieve high-precision prediction of relay electrical lifetime.The relationship between the 10 dimensional characteristic parameters such as contact resistance,coil inductance,and suction time and the electrical lifetime of the relay is analyzed.A RVM model is established to select the features of the 10 dimensional characteristic parameters,and automatically obtain the optimal feature set of the 3 dimensional characteristic parameters with the highest correlation with the electrical lifetime.These parameters are used as inputs to the SVR model to establish an electrical lifetime prediction model,a-chieving high-precision prediction of the electrical lifetime of the relay.Aimed at the problem of parameter selection in SVR model,an improved water cycle algorithm(IWCA)is proposed to globally optimize predictive performance.The experimental results show that compared to traditional BP neural network methods,the proposed combination model has higher prediction accuracy,better real-time performance,stronger generalization ability under small sample conditions and better application prospects.
万方数据
维普
