The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms.

当发电机与现有发电机并联安装时，机电振荡的概念就会在电力网络中引发。此外，多个区域的互连，传输的扩展，反复无常的负载特性等会在合并的电网中引起低频振荡。本文提出了一种基于人口的蓬勃发展的灰狼优化（GWO）算法的变体，用于对多机系统的电力系统稳定器参数进行阻尼低频振荡的调整。通过对目标函数进行框架化调整参数，考虑到具有较小阻尼比的系统状态的改进阻尼比，并将系统特征值移向s平面的左侧，以改善系统振荡的稳定特性。稳定器策略的必要性与流行算法的特点相映射，并设计了GWO的混合版本以增强多机电力系统的稳定性。根据稳定器的性能，推荐了GWO技术的四个变体，分别是改进的灰狼优化（MGWO），混合MGWO粒子群优化（MGWOPSO），混合MGWO正弦余弦算法（MGWOSCA）和混合MGWO乌鸦搜索算法（MGWOCSA）设计的多机电源网络。通过对23个基准函数的统计分析，实现了所提出的方法。非参数统计检验，即Feidman检验，Anova检验和Quade检验，已经在测试系统上进行了进一步的详细分析。在自清除故障下进行了详细的比较分析，以说明所提出技术的适用性。为了分析的目的，已经观察到系统特征值的位置以及它们的振荡频率和相应的阻尼比。此外，在考虑系统不确定性的情况下，系统状态的阻尼特性也与所提出的算法获得的PSS参数一起呈现。已在测试系统上执行，进一步详细分析。在自清除故障下进行了详细的比较分析，以说明所提出技术的适用性。为了分析的目的，已经观察到系统特征值的位置以及它们的振荡频率和相应的阻尼比。此外，在考虑系统不确定性的情况下，系统状态的阻尼特性也与所提出算法获得的PSS参数一起呈现。已在测试系统上执行，进一步详细分析。在自清除故障下进行了详细的比较分析，以说明所提出技术的适用性。为了分析的目的，已经观察到系统特征值的位置以及它们的振荡频率和相应的阻尼比。此外，在考虑系统不确定性的情况下，系统状态的阻尼特性也与所提出的算法获得的PSS参数一起呈现。已经观察到系统特征值的位置以及它们的振荡频率和相应的阻尼比。此外，在考虑系统不确定性的情况下，系统状态的阻尼特性也与所提出的算法获得的PSS参数一起呈现。已经观察到系统特征值的位置以及它们的振荡频率和相应的阻尼比。此外，在考虑系统不确定性的情况下，系统状态的阻尼特性也与所提出的算法获得的PSS参数一起呈现。