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

This paper proposes a novel photovoltaic (PV) array model using an equivalent single-diode electrical circuit with threetime-varying parameters: the diode quality factor, series resistance, and shunt resistance. The goal is to determinean optimal fuzzy inference mechanism to accurately predict the evolution of each parameter under varying climaticconditions, such as solar irradiance, cell temperature, and PV voltage. A dataset of ۵۰۰ experimental samples, collectedfrom three series-connected monocrystalline PV panels, was used—۳۰۰ samples for model training and ۲۰۰ for validation.The proposed design normalizes input data to [۰, ۱] and feeds it into an initial fuzzy mechanism with predefinedfuzzification rules. The mechanism generates normalized outputs, which are denormalized to compute predicted PVcurrents. 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 mosteffective ones. This process iterates until a final fuzzy mechanism with reduced fuzzification rules is achieved, capableof supervising the evolution of each adjustable parameter under varying climatic conditions. Experimental validationconfirms the accuracy and robustness of the proposed adjustable-parameter PV model, outperforming conventionalfixed-parameter models. This approach provides a reliable framework for PV system modeling, significantly improvingpredictive accuracy and adaptability in real-world conditions, with potential applications in advanced MPPT controllersynthesis and renewable energy system optimization.

کلیدواژه ها:

Photovoltaic modelling ، MPPT ، Fuzzy logic ، Optimization