Multi-Parameter Identification Method for Underwater Wireless Power Transfer Based on Adaptive Particle Swarm Optimization Algorithm

Online monitoring of underwater wireless power transfer (UWPT) system parameters is of great significance for improving system transmission efficiency and stability. This paper proposes a UWPT system parameter identification method based on the adaptive particle swarm optimization Algorithm (APSO) algorithm. By measuring the front-end port impedance, it enables online identification of important parameters such as parasitic parameters due to eddy current effect, load resistance, and mutual inductance, without bilateral communication. Additionally, it eliminates the need for solving the circuit matrix multi-parameter equations precisely, saving computational resources. The paper conducts a mathematical analysis of the UWPT system's eddy current effect model and constructs an optimization model for parameter identification. Finally, an experimental platform is established to verify the effectiveness of the proposed parameter identification method. The PSO algorithm and the proposed APSO algorithm are used for parameter identification of the UWPT system under different load and coil offset conditions. Experimental results show that under various operating conditions, the APSO algorithm achieves an average identification error of 0.94‰ and an average convergence count of 60. Compared to the traditional PSO algorithm, the APSO algorithm achieves higher accuracy and faster convergence speed in parameter identification.