Solar cell parameters extraction using particle swarm optimization algorithm

Hamid, Nurul Farhana Abdul; Rahim, N.A.; Selvaraj, Jeyraj

doi:10.1109/ceat.2013.6775676

Cited by 21 publications

(2 citation statements)

References 16 publications

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“…From articles [8]- [15], authors presented different techniques for extracting the parameters of SDM of PV panel. In [8], the parameters of SDM were estimated using the particle swarm optimization (PSO) technique and was compared with other techniques, proving to be highly accurate and promising based on experimental I-V data of PV cell. Firefly algorithm (FA) was proposed for estimation based on SDM in [9] and was compared with other methods from the literature.…”

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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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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“…Consequently, many metaheuristic algorithms have been developed and applied in many fields and to extract the parameters of photoelectric models. The parameters of the diode model of a polycrystalline solar module cell were extracted using the moth optimization algorithm [16], the genetic algorithm (GA), which was derived from Darwin's development theory [17], particle swarm optimization (PSO) [18], simulated annealing algorithm (SA) [19], artificial bee colony (ABC) [20], anarchic asexual reproduction (CARO) [21], multiple learning backtracking search (MLBSA) [22], cuckoo search algorithm (CS) [23], biogeography optimization (BBO) [24], MBO algorithm [25], sunflower optimization algorithm (SFO) [26], coyote optimization algorithm (COA) [27], Bacterial Foraging Optimization (BFO) [28], Harris haws optimization (HHO) [29], and modified flower algorithm (MFA) [30].…”

Section: Introductionmentioning

confidence: 99%

A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

Mahmood,

Hussein,

Aljohani

et al. 2023

Mathematics

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Harnessing solar energy efficiently via photovoltaic (PV) technology is pivotal for future sustainable energy. Accurate modeling of PV cells entails an optimization problem due to the multimodal and nonlinear characteristics of the cells. This study introduces the Multi-strategy Gradient-Based Algorithm (MAGBO) for the precise parameter estimation of solar PV systems. MAGBO incorporates a modified gradient search rule (MGSR) inspired by the quasi-Newton approach, a novel refresh operator (NRO) for improved solution quality, and a crossover mechanism balancing exploration and exploitation. Validated through CEC2021 test functions, MAGBO excelled in global optimization. To further validate and underscore the reliability of MAGBO, we utilized data from the PVM 752 GaAs thin-film cell and the STP6-40/36 module. The simulation parameters were discerned using 44 I-V pairs from the PVM 752 cell and diverse data from the STP6-40/36 module tested under different conditions. Consistency between simulated and observed I-V and P-V curves for the STM6-40/36 and PVM 752 models validated MAGBO’s accuracy. In application, MAGBO attained an RMSE of 9.8 × 10−4 for double-diode and single-diode modules. For Photowatt-PWP, STM6-40/36, and PVM 752 models, RMSEs were 2.4 × 10−3, 1.7 × 10−3, and 1.7 × 10−3, respectively. Against prevalent methods, MAGBO exhibited unparalleled precision and reliability, advocating its superior utility for intricate PV data analysis.

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