Gbadega Peter Anuoluwapo, Sun Yanxia
Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa.
Heliyon. 2023 Aug 28;9(9):e19199. doi: 10.1016/j.heliyon.2023.e19199. eCollection 2023 Sep.
This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.
本文提出了一种新颖的受自然启发的元启发式优化算法，即增强型鲸鱼优化算法（EWOA），该算法模仿座头鲸的社会行为，以解决基于随机可再生能源且配备超导磁储能（SMES）的电力系统的多区域自动负荷频率控制（LFC）问题的优化。针对传统鲸鱼优化算法（WOA）存在的收敛时间缓慢、精度低以及容易陷入局部最优等局限性，提出了EWOA算法。所研究的系统模型包括一些物理约束，如时间延迟（TD）、发电速率约束（GRC）、再热式汽轮机（RT）和死区（DB）。研究了这些物理约束对所提出控制器动态性能的影响。利用EWOA算法动态优化PID控制器的参数，以实现系统的最优性能。使用一些性能指标，将所提出控制器的有效性和动态性能与传统WOA进行了比较。系统模型在两个区域还包括超导磁储能（SMES）单元，并研究了它们对系统性能的影响。研究了系统的两个不同参数（频率偏差参数B和调速器速度调节系数R）的变化对频率偏差响应和控制器鲁棒性的影响。结果表明，所提出控制器的动态性能优于传统WOA，并且对于系统负荷、参数和阶跃负荷扰动的变化更具鲁棒性和稳定性。
