Parameter Identification Method for Wireless Power Transmission System Based on Radial Basis Function Neural Network

摘要

This paper introduces a method based on the intelligent algorithm for wireless power transmission system parameter identification. The radial basis function (RBF) neural network has been used as the algorithm model, the amplitude, and frequency of the input terminal voltage of the wireless transmission system, the amplitude of the input current and as the sample eigenvalue, the phase angle predicts the resistance value and capacitance value of the receiving end of the system, thus avoiding the complicated work measured at the receiving end. The sample data used by the algorithm is generated by calculating the exact value through the system matrix equation and then adding a certain amplitude of noise, which is close to the actual operating parameters of the wireless transmission system. The RBF neural network training process is divided into two parts: sample center point position calculation and weight value optimization. The sample center point position is calculated by the k-means clustering algorithm, and the weight value is optimized by the driven adaptive learning rate gradient. algorithm. The final test results show that the RBF neural network can predict the parameters of the wireless transmission system with high precision, and has certain anti-interference ability, good generalization performance, and can be applied in the actual wireless transmission system. At the end of the paper, the application scope of the algorithm is summarized, and the improvement and possible improvement methods of the algorithm are proposed.