Collaborative swarm intelligence to estimate PV parameters

Nunes, Hugo; Pombo, J.A.N.; Bento, Pedro; Mariano, S.J.P.S.; Calado, M.R.A.

doi:10.1016/j.enconman.2019.02.003

Cited by 93 publications

(35 citation statements)

References 62 publications

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“…The optimized parameters of the DDM and TDM based on COA have been introduced in Table VI and Table VII, respectively. Table VI is also for comparing the results of the DDM-based COA with other techniques of WDOWOAPSO [56], GCPSO [63], TVACPSO [64], and ABC-DE [65]. The table validates the superiority of the applied COA algorithm with respect to the minimum value of the RMSE which equals 2.40412239424184E-3.…”

Section: B Case Study 2; Photowatt-pwp201 Modulementioning

confidence: 70%

“…Table I also includes the results of the estimated parameters based on COA and those estimated based on other optimization techniques such as ABSO [40], HS [36], PSO [25], GA [53], An.5-Pt. [54], LW [55], Newton [56], CM [57], and PS [58]. From this table, it can be noticed that for the SDM model, the application of the proposed COA algorithm results in the minimum value of the RMSE that is equal to 7.75470161606E-04.…”

Section: Resultsmentioning

confidence: 96%

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Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

et al. 2020

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Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547x10-4 , 7.64801x10-4 , and 7.59756 x10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

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Section: B Case Study 2; Photowatt-pwp201 Modulementioning

confidence: 70%

Section: Resultsmentioning

confidence: 96%

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

et al. 2020

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“…For a single diode model, the results provided by the proposed TGA are better than that of Newton [57], PS [32], OIS [61], TSLLS [69], RF [70], and DAB [63]. The values of the PV module parameters in the DDM are more accurate than those obtained by WDOWOAPSO [64], GCPSO [65], TVACPSO [66] and ABC-DE [67] as validated by the lowest value of the RMSE.…”

Section: Parameters Of Photowatt-pwp201 Pv Modulementioning

confidence: 80%

Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules

et al. 2020

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Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for identifying the values of the unknown parameters of various solar cells and PV modules based on different diode models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer's PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters' identification of PV of solar cells.

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“…Xiong et al solved the parameter extraction problem of different PV models by using several metaheuristics including symbiotic organisms search (SOS) algorithm [24], improved WOA based on two modified prey searching strategies [25], and hybrid DE with WOA [26]. In addition to the aforementioned metaheuristics, many more [27][28][29][30][31][32][33][34][35][36][37][38][39][40] have also been presented to solve the important problem. e abovementioned metaheuristics have, to some extent, proven themselves promising methods for the parameter extraction problem of PV models.…”

Section: Introductionmentioning

confidence: 99%

Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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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.

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Collaborative swarm intelligence to estimate PV parameters

Cited by 93 publications

References 62 publications

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules

Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models

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