遗传算法的改进策略及其在非线性发电机励磁系统参数辨识中的应用

Improved strategies of genetic algorithm and their applications in parameter identification of nonlinear generator excitation systems

针对标准遗传算法收敛速度慢、易早熟等缺陷,通过对遗传策略的综合改进,提出了一种基于改进遗传算法的参数辨识方法。通过建立励磁系统原模型和标准模型,给原模型和标准模型施加相同的激励信号,以模型输出误差最小作为辨识目标,利用改进遗传算法对标准模型参数进行优化调整,最终得到满足误差要求的励磁系统标准模型参数。该方法的优点在于解决了传统的辨识方法无法对励磁系统非线性环节进行有效辨识的问题,实际励磁系统参数辨识结果表明,该方法具有较快的收敛速度和较高的辨识精度。

Synthetic improvement of strategy is carried out for standard genetic algorithm to speed up its convergence speed and avoid precocity,based on which,a parameter identification method is presented for nonlinear generator excitation system. The original system model and its standard simulation model are built and exerted by same exciting signals. With the minimal difference between the outputs of two models as the objective,the parameters of the standard model are optimized using the improved genetic algorithm to meet the requirement of error,which effectively identifies the nonlinear parts of excitation system. Test results show its faster convergence speed and higher precision.