A multiple sub-objectives experimental based approach for improved model design of photovoltaic systems

One of the key steps to accurately model photovoltaic (PV) systems is the use of metaheuristic algorithms. The technique generally utilizes experimental and estimated current-vs-voltage I(V) curves obtained under standard test conditions (STC) to perform model optimization based on a single objective function. This method has one main drawback: the model derived through this approach usually presents output results that greatly deviate from the actual experimental data when estimating the cells’ performance under climatic conditions different from those of STC. The model using this technique, therefore, becomes unreliable under varying conditions despite its efficiency at STC. This problem has been frequently observed after testing many PV technologies. Thus, in this work, a novel modeling approach based on a weighted-sum multiple sub-objectives optimization method is introduced to address the challenge. The idea is to make the objective function more general, beyond STC, so as to be able to optimize the PV model using many experimental I(V) curves with a wide range of solar irradiance at the same time. This proposed idea has not been reported in the literature to the best of our knowledge. To test the effectiveness of the novel approach, commercial BLD 200-72M monocrystalline and SW175 polycrystalline PV models are employed as a test sample. A comparison is performed between the proposed and literature modeling concepts. The final results proved that the proposed approach has a competitive performance compared to the existing literature method. The proposed approach can achieve an accuracy improvement of up to 61.980%.