基于SOGWO的电力系统稳定器参数优化

Parameter optimization of a power system stabilizer based on SOGWO

电力系统稳定器(Power System Stabilizer,PSS)是抑制电力系统低频振荡的主要手段。提出选择反向运算灰狼优化(Selected Opposition-Based Grey Wolf Optimizer,SOGWO)算法对PSS进行参数优化。首先,选择典型的PSS实现类型,并设置优化过程的目标函数。其次,利用选择反向学习算法加快搜索速度,增强灰狼算法的全局搜索性能。最后,应用IEEE四机两区域系统模型验证所提方法的有效性。此外,分别对PSS参数进行PSO、GWO、SOGWO的100次优化,由统计出的阻尼比最大值、最小值、平均值以及标准差数据可知:三种优化算法均能较好地避免陷入局部最优并快速收敛,而SOGWO优化PSS参数的鲁棒性更好。

The power system stabilizer(PSS)is a major means for suppressing low-frequency oscillation of a power system.This paper proposes a Selected Opposition-Based Grey Wolf Optimizer(SOGWO)for PSS parameter optimization.First,the typical PSS implementation type is selected and the objective function is set in the optimization process.Secondly,a selected opposition-based learning algorithm is used to accelerate the exploration speed and enhance the global search performance of the Grey Wolf Optimizer(GWO).Finally,the validity of the proposed method is verified by employing the IEEE four-machine two-area system model.In addition,PSO,GWO and SOGWO optimize PSS parameters for 100 times each.According to the data which includes maximum,minimum,mean and standard deviation of damping ratio,it can be seen that all three optimization algorithms can avoid falling into local optima and converge quickly,while SOGWO optimizes PSS parameters with better robustness.