Mohammed Abdallah, Kadry Ahmed, Abo-Adma Maged, Samahy Adel El, Elazab Rasha
Faculty of Engineering, Helwan University, Cairo, Egypt.
Sci Rep. 2025 Jan 17;15(1):2298. doi: 10.1038/s41598-025-85910-6.
Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability-a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller's effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.
由于系统不确定性和不断变化的负载需求，孤立微电网中的频率调节具有挑战性。本研究提出了一种最优μ综合鲁棒控制策略，该策略在调节微电网频率的同时提高系统性能和稳定性——这是一种通过子系统频率分析来选择性能和鲁棒性权重的固定结构方法。该控制器在不等式约束下使用多目标粒子群优化（MOPSO）和多目标遗传算法（MOGA）进行优化，采用帕累托前沿来识别最优解。对比分析表明，经MOPSO优化的控制器具有卓越的鲁棒性和性能，与传统μ综合控制器相比，能承受高达236%的不确定性，而传统控制器为171%。此外，它显著降低了频率偏差并增强了暂态响应。奈奎斯特稳定性分析证实了在可再生能源不确定性情况下的鲁棒性。结果突出了所提出的控制器在孤立微电网频率调节中的有效性，未来工作将集中于实际数字信号处理（DSP）应用的离散时间实现。
