Alipour Ebrahim, Dejamkhooy Abdolmajid, Hosseinpour Majid, Vahidnia Arash
Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
School of Engineering, RMIT University, Melbourne, Australia.
Sci Rep. 2024 Oct 1;14(1):22846. doi: 10.1038/s41598-024-73233-x.
Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.
如今，环境问题和化石燃料的局限性，以及经济效益和诸如可靠性等技术问题，促使传统配电网向智能微电网转变，在智能微电网中可再生能源与电网相融合。然而，由于分布式发电的间歇性和不确定性，系统面临新的挑战，这会导致微电网中的频率波动。光伏电池是当代微电网的主要组成部分。尽管光伏（PV）系统依赖于太阳辐照度和温度，且会受到部分阴影现象的影响，但通过适当的控制方案，它们有助于提高微电网的频率稳定性。本文针对一个混合独立微电网设计了频率控制策略，该策略对负载扰动、天气条件变化以及微电网参数的不确定性具有鲁棒性。所提出的智能控制方案基于递归自适应神经模糊推理系统（RANFIS）。采用鲸鱼优化算法（WOA）来优化RANFIS控制器结构并生成隶属函数的参数。在这个多目标优化中，目标是调节时间（ST）、超调量（Osh）和积分平方误差（ISE）。仿真结果验证了所提出的RANFIS控制器在各种运行情况下与其他控制器相比具有高鲁棒性和性能，以及可再生能源（RES）的间歇性行为，如太阳辐射的波动和微电网参数中的某些不确定性。
