DIWJAYA: JAYA driven by individual weights for enhanced photovoltaic model parameter estimation

• Published in: Energy conversion and management Vol. 305; p. 118258
• Main Authors: Choulli, Imade, Elyaqouti, Mustapha, hanafi Arjdal, El, Ben hmamou, Dris, Saadaoui, Driss, Lidaighbi, Souad, Elhammoudy, Abdelfattah, Abazine, Ismail, El aidi idrissi, Yassine
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: administrative management; algorithms; energy conversion; equations; Metaheuristic algorithms; mutation; Optimization problems; Parameter extraction; Photovoltaic modeling; solar collectors; steers; Photovoltaic modeling; Optimization problems; Parameter extraction; Metaheuristic algorithms
• DOI: 10.1016/j.enconman.2024.118258
• ISSN: 0196-8904

•Employment of individual metrics for balanced optimization.•The algorithm is broadly applicable to diverse photovoltaic models in varied environments.•The algorithm has demonstrated an overall superior performance compared to other meta-heuristic methods. The modeling of solar cells and the precise identification of their equivalent model parameters have become one of the most extensively discussed subjects in recent developments in photovoltaic systems. Numerous meta-heuristic algorithms have been devised for this purpose, among which the JAYA algorithm has gained particular popularity due to its simplicity and efficiency. Nevertheless, there are still opportunities for improvement, particularly in terms of convergence speed and the prevention of local optima. This article presents an enhanced version of JAYA tailored for the precise extraction of equivalent model parameters. Utilizing individual performance metrics, participants automatically select their search method to maintain a balance between exploration and exploitation. By introducing an individual weighting factor and the current population average into the traditional JAYA equation, members steer clear of incorrect solutions from the outset and judiciously approach the best-suggested solution, effectively discouraging local optima. A Gaussian mutation strategy also enhances population quality. The improved algorithm is applied to estimate optimal parameters for single-diode, double-diode, triple-diode, and photovoltaic module models. Statistical comparisons demonstrate the superiority of this version for photovoltaic parameter estimation, particularly in terms of stability, precision, and convergence speed.