Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm

• Published in: Environmental science and pollution research international Vol. 30; no. 15; pp. 44536 - 44552
• Main Authors: Benghanem, Mohamed, Lekouaghet, Badis, Haddad, Sofiane, Soukkou, Ammar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Basic converters; Computer Simulation; diodes; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Industrial applications; Mathematical models; Modules; Optimization; Parameter modification; Performance prediction; Photovoltaic cells; Photovoltaics; Research Article; Robustness (mathematics); Root-mean-square errors; solar collectors; Waste Water Technology; Water Management; Water Pollution Control; PV parameters; Optimization algorithm; Rao-1 algorithm; Photovoltaic modules; Chaotic
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-24941-2

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer’s datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P–V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value 7.73006208 × 10 - 4 , and in the double diode model, an RMSE of value 7.445111655 × 10 - 4 has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.