Unknown Parameter Estimation of a Detailed Solar PV Cell Model

Huynh, Duy C.; Ho, Loc D.; Dunnigan, Matthew W.

doi:10.1109/tencon50793.2020.9293714

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…The estimation of both the model parameters and MPPs of the PV module is novelly proposed in this paper which is necessary for improving the optimal exploitation efficiency of PV power systems. This combined estimation has been less mentioned in previous studies due to limitations of the analytical, numerical and meta-heuristic algorithm-based techniques [1], [2], [4] [25], [31]. Most of the previous studies have independently performed the estimation of the model parameters [2], [4] and MPPs [1], [25], [34] of the PV module.…”

Section: Siaw-pso Algorithmmentioning

confidence: 99%

“…This combined estimation has been less mentioned in previous studies due to limitations of the analytical, numerical and meta-heuristic algorithm-based techniques [1], [2], [4] [25], [31]. Most of the previous studies have independently performed the estimation of the model parameters [2], [4] and MPPs [1], [25], [34] of the PV module. This inhibits the update of variations including the model parameters and MPPs of the PV module as synchronously and quickly as possible and certainly affects the results of the optimal and adaptive operation of the PV power system.…”

Section: Siaw-pso Algorithmmentioning

confidence: 99%

“…In some other applications, the PV cell needs to be modelled in more detail than the single-diode model, the PV cell is then described by a double-diode model [4]. As the detail of the PV cell is required to be increased in some situations, the PV cell is represented by a triple-diode model [5].…”

Section: Introductionmentioning

confidence: 99%

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Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.INDEX TERMS Estimation, maximum power point, meta-heuristic algorithms, model parameter, photovoltaic modules

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Section: Siaw-pso Algorithmmentioning

confidence: 99%

Section: Siaw-pso Algorithmmentioning

confidence: 99%

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Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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“…Artificial hummingbird algorithm (AHA) [14] and genetic algorithm (GA) [15] are utilized to determine solar cell parameter values and demonstrated the accuracy and efficiency of these methods for identification of parameters of SDM. In [16], authors identified the parameters of DDM applying combination of artificial bee colony (ABC) optimization and PSO. A three-diode mode was addressed in [17] with hunter-prey optimization (HPO) and wild horse optimization (WHO) techniques.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet

Ghosh,

Mandal

2023

IEEE Open J. Instrum. Meas.

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Estimating the parameters of solar photovoltaic (PV) panels is crucial for effectively managing operations in solarbased microgrids. Various techniques have been developed for this purpose, and one accurate approach is solar cell modeling using metaheuristic algorithms from current-voltage (I-V) data of the PV panel. However, this method relies on experimental datasets, which may not be readily available for most industrial PV panels. Hence, this research proposes a new technique for estimating the parameters of different types of PV modules using only manufacturer datasheets. Additionally, three metaheuristic optimization techniques, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) optimization, and Harris Hawks Optimization (HHO), are investigated for solving this problem. The obtained results using these optimizers indicate that PSO mostly outperforms other algorithms, in terms of accuracy, while demonstrating faster computation. The proposed method is evaluated for three different PV units. Under 1000W/m 2 of irradiance and a specified temperature, the method has been validated with available experimental data sets. Furthermore, a comparative analysis with some other existing methods in the literature reveals the model's competitiveness despite not relying on experimental datasets. Also, an uncertainty analysis for the extracted parameters has shown that the obtained results are reliable enough in predicting the actual dynamics of PV units. This study holds significance for other researches on the basis of PV panel parameters, managing commercial PV power plant operation with maximum power point tracking controller, etc.

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