The problem of stability is generally caused by insufficient damping of electromechanical oscillations (EMOs). Power system stabilizers (PSSs) are the most advised and efficient devices to increase the system damping and enhance the dynamic characteristics of power networks during abnormal conditions. Unfortunately, the performance of the PSS controller is mostly dependent on the parameters of the lead-lag compensator. Within this context, this study presents a new chaotic-based sunflower optimization algorithm with local search (CSFO-LS) for optimum design of PSS controllers. In the proposed algorithm symbolized by CSFO-LS, the random parameters of the original SFO are substituted by chaotic sequences to avoid premature convergence at local optima and improve the accuracy of the optimum solution. Firstly, the CSFO-LS is tested and evaluated on various benchmark functions with different characteristics such as multimodality, separability and regularity. Then, it is applied for selecting the optimum parameters of the PSS controllers. These parameters are tuned in order to shift all electromechanical modes in a pre-specified zone in the left side of the s-plan. Simulation results based on eight benchmark functions show that CSFO-LS outperforms all the algorithms used for comparison. Moreover, to demonstrate the applicability and performance of the proposed method for providing good damping of low frequency oscillations, a standard power system test under various operating conditions and severe fault is used. Obtained results are compared with those obtained using the original SFO and other recent optimization techniques. In this study, an improved version of the SFO is proposed for providing optimum EMOs damping. All EMOs have to be shifted as much as possible to the left side of the s-plan instead of shifting them to a fixed zone. To our best knowledge, this technique is not suggested or used for any power system problem.

中文翻译：

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电力系统稳定器优化调整的新混沌向日葵优化算法

稳定性问题通常是由机电振荡 (EMO) 阻尼不足引起的。电力系统稳定器（PSS）是在异常情况下增加系统阻尼和增强电网动态特性的最推荐和最有效的装置。不幸的是，PSS 控制器的性能主要取决于超前滞后补偿器的参数。在此背景下，本研究提出了一种新的基于混沌的向日葵优化算法和局部搜索 (CSFO-LS)，用于 PSS 控制器的优化设计。所提出的以CSFO-LS为符号的算法中，将原始SFO的随机参数替换为混沌序列，避免了局部最优处的早熟收敛，提高了最优解的精度。首先，CSFO-LS 在具有不同特征的各种基准函数上进行测试和评估，例如多模态、可分离性和正则性。然后，它被应用于选择 PSS 控制器的最佳参数。这些参数经过调整，以便在 s 计划左侧的预先指定区域中移动所有机电模式。基于八个基准函数的仿真结果表明，CSFO-LS 优于所有用于比较的算法。此外，为了证明所提出的用于提供良好的低频振荡阻尼的方法的适用性和性能，使用了在各种运行条件和严重故障下的标准电力系统测试。将获得的结果与使用原始 SFO 和其他最近的优化技术获得的结果进行比较。在这项研究中，提出了 SFO 的改进版本，以提供最佳的 EMO 阻尼。所有 EMO 都必须尽可能地转移到 s-plan 的左侧，而不是将它们转移到固定区域。据我们所知，此技术不建议或用于任何电力系统问题。