Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover

Hamid, Naima; Abounacer, Rachida; Oumhand, Mohammed Idali; Feddaoui, M’barek; Agliz, Driss

doi:10.1080/01430750.2017.1421577

Cited by 41 publications

(10 citation statements)

References 27 publications

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“…CI techniques were also suggested for parameter identification of solar cell models because of their ability to solve complex and non-linear problems. In [30], genetic algorithms (GAs) were successfully applied for identifying solar cell parameters. In [31], an efficient approach based on the salp swarm algorithm (SSA) was presented for extracting the parameters of the equivalent circuit of solar cells.…”

Section: B Parameter Identification For Solar Cell Modellingmentioning

confidence: 99%

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

Moyo

Dewa

2022

Ren. Ener. & Sust. Dev.

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The use of computational intelligence (CI) in solar photovoltaic (SPV) systems has been on the rise due to the increasing computational power, advancements in power electronics and the availability of data generation tools. CI techniques play an important role in modelling, sizing, forecasting, optimizing, analysing and predicting the performance and control of SPV systems. Thus, CI techniques have become an essential technology as the energy sector seeks to meet the rapidly increasing demand for clean, cheap, and reliable energy. In this context, this review paper aims to investigate the role of CI techniques in the advancements of SPV systems. The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking of SPV systems. A performance comparison between CI techniques and conventional methods is also carried out toprove the importance of CI in SPV systems. The findings confirmed the superiority of CI techniques over conventional methods for every application studied and it can be concluded that the continuous improvements and involvement of these techniques can revolutionize the SPV industry and significantly increase the adoption of solar energy.

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Section: B Parameter Identification For Solar Cell Modellingmentioning

confidence: 99%

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

Moyo

Dewa

2022

Ren. Ener. & Sust. Dev.

View full text Add to dashboard Cite

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“…Lately, active investments target the PV production outstandingly, and many revenues are advised to research in PV energy. Usually, such regulation arises from the severe cost reduction in the PV operation parts, which designates the enormous development of the PV industry [4], [5]. The cost of PV machines pointedly fell within the recent decade, including the PV operation itself with its design and adapters [6].…”

Section: Introductionmentioning

confidence: 99%

Modified Interactive Algorithm Based on Runge Kutta Optimizer for Photovoltaic Modeling: Justification Under Partial Shading and Varied Temperature Conditions

et al. 2022

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The accuracy of characteristic the PV cell/module/array under several operating conditions of radiation and temperature mainly relies on their equivalent circuits sequentially; it is based on identified parameters of the circuits. Therefore, this paper proposes a modified interactive variant of the recent optimization algorithm of the rung-kutta method (MRUN) to determine the reliable parameters of single and double diode models parameters for different PV cells/modules. The results of the MRUN optimizer are validated via series of statistical analyses compared with five new meta-heuristic algorithms including aquila optimizer (AO) , electric fish optimizer (EFO), barnacles mating optimizer (BMO), capuchin search algorithm (CapSA), and red fox optimization algorithm (RFSO) moreover, twenty-five state-of the art techniques from literature. Furthermore, the identified parameters certainty is evaluated in implementing the characteristics of an entire system consists of series (S), and series-parallel (S-P) PV arrays with numerous dimensions. The considered arrays dimensions are three series (3S), six series (6S), and nine series (9S) PV modules. For the investigated arrays, three-dimensional arrays are recognized. The first array comprises 3S-2P PV modules where two parallel strings (2P) have three series modules in each string (3S). The second array consists of six series-three parallel (6S-3P) PV modules, and the third one has nine series-nine parallel (9S-9P) PV modules. The results prove that the proposed algorithm precisely and reliably defines the parameters of different PV models with root mean square error and standard deviation of 7.7301e −4 ± 4.9299e −6 , and 7.4653e −4 ± 7.2905e −5 for 1D, and 2D models, respectively meanwhile the RUN have 7.7438e −4 ± 3.5798e −4 , and 7.5861e −4 ± 4.1096e −4 , respectively. Furthermore MRUN provided extremely competing results compared to other well-known PV parameters extraction methods statistically as it has .

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“…However, Genetic Algorithms (GA) are the most widely adopted solution for the parameter estimation in PV systems. For example, the work reported in [19] proposes a new variant of the GA, which integrates a new crossover operation to maintain a good balance between the intensification of the best solutions and the diversification of the search space; such a solution was designed to identify the electrical parameters of different PV cell models (SDM and DDM). Similarly, in [20] the authors extract the solar cell parameters for a Kyocera panel (KC200GT) using GA.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Electrical Models for Photovoltaic Cells under Uniform and Partial Shading Conditions

et al. 2022

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This paper compares the performance of three electrical models (the single diode model, the Bishop model, and the Direct–Reverse model) in representing photovoltaic cells. Such comparison is performed in both the first quadrant (positive cell voltage and current—Q1) and the second quadrant (negative cell voltage and positive cell current—Q2). The analysis conducted here is based on the I–-V curves of a PV cell obtained experimentally. The parameters of each model are estimated using a Genetic Algorithm. The root mean square error and the mean absolute percentage error are computed to validate the estimation stage. Likewise, the behavior of each parameter of the models is analyzed by calculating their mean and standard deviation. Some places of interest on the I–V curve, such as the short–circuit point, the open–circuit point, and the maximum power point, are also estimated and compared.

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Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover

Cited by 41 publications

References 27 publications

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

Modified Interactive Algorithm Based on Runge Kutta Optimizer for Photovoltaic Modeling: Justification Under Partial Shading and Varied Temperature Conditions

Analysis of Electrical Models for Photovoltaic Cells under Uniform and Partial Shading Conditions

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