Hybrid Artificial Neural Network and Perturb & Observe Strategy for Adaptive Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Braulio Cruz, Luis Ricalde (), Roberto Quintal-Palomo, Ali Bassam and Roberto I. Rico-Camacho
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Braulio Cruz: Facultad de Ingenieria, Universidad Autonoma de Yucatan, Merida 97302, Mexico
Luis Ricalde: Facultad de Ingenieria, Universidad Autonoma de Yucatan, Merida 97302, Mexico
Roberto Quintal-Palomo: Facultad de Ingenieria, Universidad Autonoma de Yucatan, Merida 97302, Mexico
Ali Bassam: Laboratorio de Modelado y Optimizacion de Procesos Energeticos y Ambientales, Facultad de Ingenieria, Universidad Autonoma de Yucatan, Merida 97302, Mexico
Roberto I. Rico-Camacho: CRS Industrial Power Equipment, Plant 1, Calle 23 311, Itzincab 97390, Mexico

Energies, 2025, vol. 18, issue 19, 1-26

Abstract: Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability.

Keywords: photovoltaic systems; partial shading; maximum power point tracking; artificial neural networks; dynamic shading adaptation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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