Abstract
To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT), this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.
Highlights
Traditional electric power transmission device mainly transmits directly connected via the metal wire, and this kind of connection will cause many problems [1]
To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT), this paper proposed an improved genetic simulated annealing algorithm
Wireless power transmission is divided into electromagnetic induction, magnetic coupling resonant, and radio waves according to the power transmission principle [1]
Summary
Traditional electric power transmission device mainly transmits directly connected via the metal wire, and this kind of connection will cause many problems [1]. Magnetic coupling resonant is a new kind of wireless power transmission technology that is proposed by MIT’s Professor Marin Soljacic in 2007 [5]. Magnetic coupling resonant wireless power transmission is concerned with the far transmission distance, safety, reliability, high efficiency, and many other advantages. The traditional methods are based on the system function model derivative, which does not fully consider the constraints of the components’ working conditions for the optimized parameters, so the parameters cannot be directly used in practice and need to be further amended. In order to achieve the maximums of transmission efficiency and distance, respectively, this paper fully considers the constraints of the system to establish the mathematical model and proposes an improved genetic simulated annealing algorithm to optimize parameters simultaneously. The comparison shows that the optimization ability of IGSA is better than the general algorithm and ANSYS finite-element software is used for simulation verification
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