Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules

• Published in: ISA transactions Vol. 116; pp. 139 - 166
• Main Authors: Premkumar, M., Jangir, Pradeep, Sowmya, R., Elavarasan, Rajvikram Madurai, Kumar, B. Santhosh
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2021
• Subjects: Chaotic; Double-diode model (DDM); Parameters identification; Photovoltaics; Self-adaptive weight; Single-diode model (SDM); Double-diode model (DDM); Self-adaptive weight; Single-diode model (SDM); Parameters identification; Chaotic; Photovoltaics
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2021.01.045

Parameters for defining photovoltaic models using measured voltage–current​ characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation’s best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper’s source code at https://premkumarmanoharan.wixsite.com/mysite. •A new CJAYA algorithm is proposed to estimate the parameters of SDM and DDM models.•Chaotic and self-adaptive weights are combined with JAYA to get the best solution.•The performance of CJAYA and its variants are tested on 23 benchmark functions.•Four PV models are considered to validate the performance of CJAYA and its variants.•Detailed analysis, results, and statistical tests show that the CJAYA is superior.