Abstract: With the rapid development of new energy systems and the significant increase in power consumption,users put forward new requirements for the performance and reliability of contactors.The optimal design of contactors becomes a hot issue.Restricted by the low efficiency for computation of contactor performance,intelligent optimization algorithms with strong global search ability can’t be well applied in contactor optimization design.That limits the development of contactor optimization design so it is necessary to study the fast calculation algorithms of contactor performance.In this paper,an approximate model for contactors was proposed,which is based on radial basis function (RBF) neural network.The parameters of the RBF neural network are optimized by RPCL and PSO to ensure the accuracy of the model.Taking the static and dynamic characteristics of a high-power contactor as an example,it takes 40 seconds for an approximate model to calculate the dynamic characteristics,while nearly one day for the commonly used finite element method.The error of the approximate model is less than 5%.In general,the model greatly improves the efficiency of computation of the performance of contactors and lays the foundation of optimal design of contactors.

Key words: contactor, radial basis function (RBF) neural network, RPCL, PSO, finite element method