This paper proposes a novel hybrid technique called enhanced grey wolf optimization‐sine cosine algorithm‐cuckoo search (EGWO‐SCA‐CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well‐balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state‐of‐the‐art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self‐clearing three‐phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.

中文翻译：

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系统不确定性下用于PSS参数调整的智能EGWO‐SCA‐CS算法

本文提出了一种新的混合技​​术，称为增强灰狼优化-正弦余弦算法-布谷鸟搜索（EGWO-SCA-CS）算法，以提高电力系统的稳定性。所提出的方法包括增强和混合形式的流行灰太狼优化（GWO）。它包含使用杜鹃搜索（CS）算法的均衡平衡的勘探和开发，并通过正弦余弦算法（SCA）增强了搜索能力，从而避免了陷入局部最优问题。所提出的技术已通过23个基准函数进行了验证，并与最新方法进行了比较。基准函数由单峰，多峰函数组成，从中可以确定所提出技术的最佳适用性。鲁棒性分析还通过箱线图与提出的方法进行了比较，并针对30个单独的运行进行了详细的统计分析。从基准函数收集的推论中，将所提出的技术应用于电力系统的稳定性问题，该问题由于负载的非线性变化以及由此产生的工作条件而受到很大的压力。对于多机系统的数学模型，已经考虑了动力系统组件的动力学特性，并且在优化最佳控制器参数时对多目标函数进行了设计。通过考虑两个案例研究来评估所提出算法的有效性，即（i）最佳控制器参数调整，以及（ii）振动阻尼装置在电力系统稳定性增强中的协调。在第一个案例研究中 电力系统稳定器（PSS）被视为控制器，而自清除三相故障被视为系统不确定性。相比之下，静态同步补偿器（STATCOM）和PSS被视为要协调的控制器，而在第二个案例研究中，系统状态的扰动被视为不确定性。