非接触电能传输系统参数优化的改进遗传解法

为了解决非接触电能传输系统设计中的参数优化问题,提出了一种混合改进遗传解法.首先建立了非线性规划数学模型,其中对频率稳定性约束条件进行了修正;其次在遗传算法中采用虫口模型产生优良的混沌初始种群,采用"两次归一化"来处理目标及约束函数,并利用可行性规则代替罚函数法来选择优良个体以减少额外的经验参数;最后设计了均匀变异加高斯变异的混合变异算子以提高算法的全局搜索能力.仿真及实验结果表明:改进后的算法能够较好地突破局部最优解的限制,较快地找到了系统的全局最优参数;优化后的系统达到了设计要求,且对于负载在约束范围内的动态大范围变化有较强的鲁棒性,如当负载增大为原来的2倍时,输出电压及原边电流的大小及频率基本保持不变.

Improved Genetic Algorithm for Parameter Optimization in Contactless-Power-Transfer System

For solving parameter optimization problem in contactless-power-transfer(CPT) system design,a hybrid refined genetic-algorithm(GA) method is proposed.A nonlinear programming(NLP) model is established where the frequency stability constraint condition is modified,then logistic model is considered to produce the fine chaotic initial population in GA,and the two-time normalization is proposed to deal with objective function and constraint functions.And feasibility rules(FR) are used instead of penalty function to choose fine individuals to reduce additional empirical parameters,and a mixture of uniform mutation and Gaussian mutation is designed to improve the global searching capability.The simulation and experiment show that the refined method is endowed with better ability to break the local optimum limitations and to find global optimum parameters quickly,and the optimized system meets design requirements with good robustness when load changes in a large scale within the constraint range.For instance,as the load increases to 2 times of the original,the amplitude and frequency of output voltage and primary current remain unchanged.