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- 摘要
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Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts
摘要： In this research, a diversification-enhanced Harris hawks optimization (HHO) is proposed based on the chaotic drifts in the vicinity of the best agent and an opposition-based exploratory strategy to efficiently identify unknown parameters of photovoltaic models modules. This novel technique is employed to estimate the parameters of solar cells models for single diode, double diode, and photovoltaic module. Also, different temperatures and irradiation levels for three practical manufactures datasets are investigated. Competitive and statistical experimental results demonstrate the excellent traits of the proposed HHO-based identifier in estimating the key parameters of photovoltaic models compared to several well-regarded competitors that prominently performs a good performance for solving this case. Also, satisfactory stability level is archived at different temperatures and levels of irradiance due to a better interchange between diversification and intensification drifts of the proposed variant. Therefore, HHO-based identifier provides high-quality solutions for parameter identification cases based on its coherent searching trends. We recommend this approach as a competent and auxiliary technique for parameters estimation of photovoltaic models.
关键词： Swarm-intelligence,Exploration and exploitation,Photovoltaic models,Parameters estimation,Harris Hawks Optimization
更新于2025-09-19 17:13:59
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Comparative study on parameter extraction of photovoltaic models via differential evolution
摘要： Parameter extraction of photovoltaic (PV) models, which remains a multi-variable, nonlinear, and multi-modal problem, has recently gained considerable attention in the simulation and calculation of solar PV systems. Among various parameter extraction techniques, differential evolution (DE) and its variants are envisaged to be pretty effective for parameter extraction of PV models. In this paper, 11 state-of-the-art DE algorithms are comprehensively compared to extract the parameters of different PV models. The performance of each algorithm is evaluated based on the accuracy of solution, convergence speed, and the robustness. Based on the experimental results and analysis of different DE algorithms, the useful insights are concluded, which can guide the improvement of designing more efficient alternative DE methods for solving the PV parameter extraction problems.
关键词： Comparative study,Parameter extraction,Differential evolution,Photovoltaic models
更新于2025-09-19 17:13:59
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Evolutionary multi-task optimization for parameters extraction of photovoltaic models
摘要： As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.
关键词： Differential evolution,Parameter extraction,Evolutionary multi-task optimization,Photovoltaic models
更新于2025-09-16 10:30:52
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Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models
摘要： Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.
关键词： parameter extraction,cobweb model,solar photovoltaic models,supply-demand-based optimization,metaheuristic
更新于2025-09-11 14:15:04
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Extraction of Uncertain Parameters of Double-Diode Model of a Photovoltaic Panel Using Simulated Annealing Optimization
摘要： In this article, our goal is to improve the estimation of the parameters of solar photovoltaic models by using the simulated annealing (SA) algorithm. The proposed approach takes into account the uncertainties of measurements. This approach consists of three steps. The ?rst is the extraction of the parameters in a conventional manner based on SA. Then, in order to reduce the search interval of parameters, we will determine the uncertainties of the measurements of each parameter. Finally, we will determine the instantaneous parameters, taking into account the results of the ?rst two steps. For the validation of proposed theoretical developments, the proposed approach is applied to two di?erent commercial solar panel parameter estimation problems (the monocrystalline solar module STM6-40/36 and the polycrystalline silicon cells photovoltaic module Sharp ND-R250A5). The results obtained are compared with well-established algorithms to con?rm its e?ectiveness. These comparisons have shown that the proposed method exhibits largely more e?ective performances than existing methods in the literature.
关键词： uncertainties,polycrystalline silicon cells,solar photovoltaic models,simulated annealing,monocrystalline solar module,parameter estimation
更新于2025-09-11 14:15:04
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Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models
摘要： With the increasing demand for solar energy, accurate, reliable, and efficient parameters extraction of photovoltaic models is becoming more significant and difficult. Accordingly, a more accurate and robust algorithm is continuously needed for this problem. To this end, a classified perturbation mutation based particle swarm optimization algorithm is proposed in this paper. During each generation of the proposed algorithm, the performance of each updated personal best position is evaluated and quantified to be a high-quality or low-quality. Then, for the high-quality personal best position, a mutation strategy with smaller perturbation is developed to enhance the local search ability within the promising search area. For the low-quality personal best position, a bigger perturbation mutation strategy is designed to explore different regions for improving the population diversity. Furthermore, the damping bound handling strategy is employed to mitigate the issue of falling into local optimal. The effectiveness of the proposed algorithm is evaluated by extracting parameters of five different photovoltaic models, and also tested on photovoltaic models under different conditions. Experiment results comprehensively demonstrate the superiority of the proposed algorithm compared with other well-established parameters extraction methods in terms of accuracy, stability, and rapidity.
关键词： Perturbation mutation,Photovoltaic models,Particle swarm optimization,Parameters extraction
更新于2025-09-11 14:15:04