改进遗传算法在发电机励磁系统参数辨识中的应用

提出了一种基于改进遗传算法的将发电机励磁系统原模型转换为仿真软件下标准模型的参数辨识方法,以原模型和标准模型的输出误差最小作为辨识目标,利用改进遗传算法不断优化调整标准模型中的参数,最终得到满足要求的励磁系统标准模型参数。与传统的励磁系统参数辨识方法相比较,该方法很好地解决了励磁系统非线性环节难以有效辨识的问题,方便可靠,精度高。实际发电机励磁系统参数辨识结果表明,该方法具有很好的效果。

Application of improved genetic algorithm in parameter identification of generator excitation systems

A new method based on improved genetic algorithm for identifying parameters of generator excitation system is introduced in this paper. This method is used to convert the original model of generator excitation system to the standard model of excitation system. By using improved genetic algorithm, with the minimal difference between the output of the original model and that of the standard model being the object, the parameters of generator excitation system are adjusted constantly. Finally, the most suitable parameters of generator excitation system can be obtained. Compared with traditional identification methods, this method solves the problem that nonlinear part of excitation system is difficult to identify, and the result is more accurate. The actual identification result shows that this method can obtain the accurate parameters of the standard model of generator excitation system.