P Odelu, Shiva Chandan Kumar, Sen Sachidananda, Basetti Vedik, Reddy Chandra Sekhar
Department of Electrical and Electronics Engineering, SR University, Warangal, 506371, Telangana, India.
Department of Electrical Power and Control Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia.
Sci Rep. 2025 Aug 17;15(1):30128. doi: 10.1038/s41598-025-98976-z.
Ensuring stable frequency regulation in islanded airport microgrids is a challenging task owing to the intermittency of renewable energy sources, unpredictable load variations, and nonlinear dynamics. Conventional optimization techniques often struggle with premature convergence and sub-optimal controller tuning, leading to a poor transient response and inadequate frequency stabilization. These challenges necessitate an advanced optimization strategy that can efficiently handle dynamic airport environments while ensuring enhanced frequency stability. To address these issues, this study proposes a Chaotic Chimp Mountain Gazelle Optimizer (CCMGO) algorithm. The CCMGO algorithm integrates the exploration capabilities of the Chimp Optimization Algorithm (ChOA) with the fast convergence of the Mountain Gazelle Optimizer (MGO), which is further enhanced by chaotic mapping to improve search diversity and avoid local optima. The effectiveness of the proposed CCMGO optimized dynamic controller was evaluated under various load perturbation scenarios, including impulse, step-ramp, and stochastic disturbances. The system considered here is a multi-source airport model integrating wave, wind, solar, biogas turbines, battery energy storage systems, ultra-capacitors, and electric vehicles. Simulation results demonstrates that the CCMGO optimized fractional order proportional-integral-derivative controller exhibits better performances compared to the conventional genetic algorithm and particle swarm optimization based controllers, as well as contemporary metaheuristic algorithms like grey wolf optimizer and whale optimization algorithm. The proposed methodology achieves notable reductions in frequency deviation, shorter settling time, and enhanced transient response characteristics.
由于可再生能源的间歇性、不可预测的负载变化以及非线性动力学，确保孤岛机场微电网中的频率稳定调节是一项具有挑战性的任务。传统的优化技术常常难以避免过早收敛和控制器调优次优的问题，导致暂态响应不佳和频率稳定不足。这些挑战需要一种先进的优化策略，该策略能够在确保增强频率稳定性的同时，有效应对动态的机场环境。为了解决这些问题，本研究提出了一种混沌黑猩猩山地瞪羚优化器（CCMGO）算法。CCMGO算法将黑猩猩优化算法（ChOA）的探索能力与山地瞪羚优化器（MGO）的快速收敛相结合，并通过混沌映射进一步增强，以提高搜索多样性并避免局部最优。在包括脉冲、阶跃斜坡和随机干扰在内的各种负载扰动场景下，评估了所提出的CCMGO优化动态控制器的有效性。这里考虑的系统是一个集成了波浪、风能、太阳能、沼气轮机、电池储能系统、超级电容器和电动汽车的多源机场模型。仿真结果表明，与传统的遗传算法和基于粒子群优化的控制器以及当代元启发式算法（如灰狼优化器和鲸鱼优化算法）相比，CCMGO优化的分数阶比例积分微分控制器具有更好的性能。所提出的方法在频率偏差、调节时间和增强的暂态响应特性方面实现了显著降低。
