Dynamic PV Modeling Using Fuzzy Logic and Genetic Algorithm for Enhanced Monocrystalline Array Performance

Saci, Zehour Ismahane; Bechouat, Mohcene; Sedraoui, Moussa; Ayaz, Ramazan

doi:10.22111/ijfs.2025.51625.9126

Document Type : Research Paper

Authors

¹ Department of Automation and Electromechanics, Faculty of Science and Technology, University of Ghardaia, Ghardaia, Algeria.

² Department of Automation and Electromechanics, Faculty of Science and Technology, University of Ghardaia, Ghardaia, Algeria

³ Department of Electrical and Automation Engineering, PI: MIS Problem inverse: modeling information and systems, University 8 Mai 1945, Guelma, Algeria.

⁴ Department of Electrical Engineering, Davutpasa Campus,Yildiz Technical University, Istanbul 34220, Turkey

https://doi.org/10.22111/ijfs.2025.51625.9126

Abstract

This paper proposes a novel photovoltaic (PV) array model using an equivalent single-diode electrical circuit with three
time-varying parameters: the diode quality factor, series resistance, and shunt resistance. The goal is to determine
an optimal fuzzy inference mechanism to accurately predict the evolution of each parameter under varying climatic
conditions, such as solar irradiance, cell temperature, and PV voltage. A dataset of 500 experimental samples, collected
from three series-connected monocrystalline PV panels, was used—300 samples for model training and 200 for validation.
The proposed design normalizes input data to [0, 1] and feeds it into an initial fuzzy mechanism with predefined
fuzzification rules. The mechanism generates normalized outputs, which are denormalized to compute predicted PV
currents. These are compared to actual measurements to calculate modeling errors, aggregated as mean squared errors
(MSEs). A genetic algorithm (GA) minimizes the MSEs by optimizing the fuzzification rules, retaining only the most
effective ones. This process iterates until a final fuzzy mechanism with reduced fuzzification rules is achieved, capable
of supervising the evolution of each adjustable parameter under varying climatic conditions. Experimental validation
confirms the accuracy and robustness of the proposed adjustable-parameter PV model, outperforming conventional
fixed-parameter models. This approach provides a reliable framework for PV system modeling, significantly improving
predictive accuracy and adaptability in real-world conditions, with potential applications in advanced MPPT controller
synthesis and renewable energy system optimization.

Keywords

Main Subjects

Engineering

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Dynamic PV Modeling Using Fuzzy Logic and Genetic Algorithm for Enhanced Monocrystalline Array Performance

References

References

Volume 22, Issue 5September and October 2025Pages 143-158

Volume 22, Issue 5
September and October 2025
Pages 143-158