Deghfel Nassir, Badoud Abd Essalam, Merahi Farid, Bajaj Mohit, Zaitsev Ievgen
Setif Automatic Laboratory, Electrical Engineering Department, Ferhat Abbas University Setif 1, Setif, Algeria.
Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India.
Sci Rep. 2024 Mar 21;14(1):6827. doi: 10.1038/s41598-024-57610-0.
Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller's performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm's performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.
最近，将可再生能源，特别是光伏（PV）系统集成到电网中对于可持续能源发电而言变得愈发重要。研究人员已经研究了不同的最大功率点跟踪（MPPT）控制算法，以提高光伏系统的效率。本文提出了一种创新方法，用于解决在迅速变化的天气条件下光伏系统中的最大功率点跟踪问题。MPPT技术在辐照度波动和环境温度期间为负载提供最大功率。基于麻省理工学院（MIT）规则开发并设计了一种新颖的最优模型参考自适应控制器，以快速无波动地寻找全局最大功率。所提出的控制器还通过两种流行的元启发式算法进行了优化：遗传算法（GA）和鲸鱼优化算法（WOA）。这些元启发式方法已被用于克服选择MRAC控制器自适应增益的困难。在本研究中，通过自适应神经模糊推理系统生成MPPT的参考电压。在不同的温度和辐射条件下，通过MATLAB/Simulink软件测试了所提出控制器的性能。使用与升压转换器耦合的Soltech 1sth-215-p模块进行仿真，该模块为电阻性负载供电。此外，为了强调所推荐算法的性能，在使用GA和WOA的最优MRAC与传统的增量电导（INC）方法之间进行了对比研究。
