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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.

Titel:	A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems.
Autor/in / Beteiligte Person:	Deghfel, N ; Badoud, AE ; Merahi, F ; Bajaj, M ; Zaitsev, I
Link:	Full Text Finder (Volltext)
Zeitschrift:	Scientific reports, Jg. 14 (2024-03-21), Heft 1, S. 6827
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2024
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-024-57610-0
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Sci Rep] 2024 Mar 21; Vol. 14 (1), pp. 6827. <i>Date of Electronic Publication: </i>2024 Mar 21. References: Lyu, W. et al. Impact of battery electric vehicle usage on air quality in three Chinese first-tier cities. Sci. Rep. 14, 21. https://doi.org/10.1038/s41598-023-50745-6 (2024). (PMID: 10.1038/s41598-023-50745-63816760010761960) ; Li, P., Hu, J., Qiu, L., Zhao, Y. & Ghosh, B. K. A distributed economic dispatch strategy for power-water networks. IEEE Trans. Control Netw. Syst. 9, 356–366. https://doi.org/10.1109/TCNS.2021.3104103 (2022). (PMID: 10.1109/TCNS.2021.3104103) ; Duan, Y., Zhao, Y. & Hu, J. An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: Modeling, optimization and analysis. Sustain Energy Grids Netw. 34, 101004. https://doi.org/10.1016/j.segan.2023.101004 (2023). 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A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems.