A Neural Network-Based Online Parameter Identification Method for Fractional-Order Power Electronics

Component parameters determine the modeling and control accuracy, and affect the performance and reliability of a power electronic system. Therefore, online identification of component parameters is very important. The existing identification methods mostly adopt the integer-order model, which is low in precision and inconsistent with practice. To solve these issues, the fractional-order model (FOM) of the buck converter is established by fractional calculus in this paper. Comparing the similarities in structure between the FOM and neural network, an identification method with the neural network is proposed. The method can realize the online identification of capacitance and order with high accuracy and fast convergence speed. Additionally, the identification results are influenced by the memory length of the FOM and the learning rate of the neural network. Finally, the experimental platform for parameter identification of buck converters is built, and the simulation and experimental results both verify the effectiveness of the proposed method.