Abstract
This paper introduces a novel, self-tuning equivalent circuit model for polycrystalline photovoltaic (PV) modules to overcome the accuracy limitations of conventional fixed-parameter models under dynamic climatic conditions. The proposed model features dynamically adjustable parameters-the diode quality factor, series resistance, and shunt resistance-that evolve with changes in solar irradiance, temperature, and wind speed. The design of this adaptive model is achieved through a dedicated two-stage hybrid methodology. First, an artificial neural network (ANN) trained via the Back-Propagation Neural Network (BPNN) technique on 300 experimental samples establishes an initial mapping from climatic inputs to the optimal electrical parameters. The primary force of the proposed hybridization is that the optimal biases and weights from this ANN-BPNN model are then used to meticulously initialize the Particle Swarm Optimization (PSO) algorithm. In the second stage, this PSO algorithm, leveraging its superior initial population, performs a refined minimization of the Mean Squared Error (MSE) between the predicted and measured PV currents to yield the final, high-precision model. For validation, this model is compared against a conventional fixed-parameter model optimized with the same PSO parameters. The results demonstrate a dramatic accuracy improvement: the proposed dynamic model achieves an MSE of 0.0387, a 90.82% reduction compared to the conventional model's MSE of 0.4226. The key novelty lies in this effective ANN-PSO hybridization, which ensures robust and accurate dynamic modeling, providing a superior foundation for advanced PV system applications like maximum power point tracking (MPPT).