A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer

Kler, D. S.; Goswami, Y. C.; Rana, K.P.S.; Kumar, Vineet

doi:10.1016/j.enconman.2019.01.102

Cited by 101 publications

(38 citation statements)

References 64 publications

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“…The adopted limits of these 9 parameters per each module under study are summarized in Table 2. It is worth mentioning that these limits are same used in the literature to avoid biased comparisons which are extracted from references 49‐57.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…The upper and lower limits of the design variables of the PV model play an important role through the optimization process that for sure affecting the results achieved. A detailed explanation for choosing the bounds of different unknown PV parameters is carried out in references 56, 57. To avoid the repetition, the authors would like to invite the readers to refer to the valuable analysis in these later mentioned references.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…To verify the validity of the AEFA‐PV model, its optimal control variables are compared with conventional and recent metaheuristic based ODM and DDM for this module such as GCPSO, 16 TVACPSO, TLBO, GWO and ICA, 17 ISCE, 24 IWOA, 25 FFO, 40 NMSOLMFO, 52 PPSO, 55 HISA, 56 EHA‐NMS, 58 TSLLS, 59 and RF 60 as depicted in Table 3 (It may be useful declaring that the presented values of TDM model parameters are per module). It is a well‐meaning for remarking here that these optimal parameters are very near enough each other.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…The aim of this case study is to demonstrate the performance of the proposed AEFA-PV methodology in characterizing the behaviors of TDM under various operating conditions such as changeable sun irradiance levels and temperature variations. Experimental dataset points of this module are made up of 20 test points at 1000 W/m 2 and 51 C. 24,53 The optimum values of the 9 undefined parameters of TDM are given in Table 5 in comparisons with the best RMSE obtained by others such as ICSE, 24 SDA, 49 HISA, 56 EHA-NMS, 58 TSLLS, 59 and RF 60 from web page, 61 IADE, 62 ELPSO, CPSO, BSA, and ABC, 63 CIABC, 65 BMO, STLBO, and CWOA, 66 SSA, 67 and SDO, and TLABC. 68 The trend of convergence patterns toward the minimum fitness value is revealed in Figure 3.…”

Section: Case (2): Stm6-40/36 Pv Modulementioning

confidence: 99%

See 3 more Smart Citations

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

2020

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This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.

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Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Case (2): Stm6-40/36 Pv Modulementioning

confidence: 99%

See 2 more Smart Citations

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

2020

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“…Regarding the popular metaheuristic algorithms, simulated annealing algorithm [12], genetic algorithm [13,14], particle swarm optimization algorithm [15,16], differential evolution algorithm [17][18][19][20], pattern search [21], artificial bee colony algorithm [22] are widely used for the SCPIP. In addition to these well-known heuristic algorithms, there exist several papers in the literature which consider more recent approaches, such as bacterial foraging algorithm [23,24], teaching-learning-based optimization algorithm [25][26][27], biogeography-based optimization algorithm [28], chaos optimization algorithm [29], artificial fish swarm algorithm [30], bird mating optimizer approach [31], artificial immune system [32], evolutionary algorithm [1], cat swarm optimization algorithm [33], moth-flame optimization algorithm [5], JAYA optimization algorithm [34,35], chaotic whale optimization algorithm [36], imperialist competitive algorithm [37], bee pollinator flower pollination algorithm [38], shuffled complex evolution algorithm [39], memetic algorithm [40], interior search algorithm [41], collaborative swarm intelligence approach [42], and cuckoo search algorithm [43]. On the other hand, it has been proven by No-Free-Lunch theorem [44] that none of these algorithms is able to solve all type of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I−V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.

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Parameter estimation of triple diode photovoltaic model using an artificial ecosystem‐based optimizer

El‐Dabah

El‐Sehiemy

Becherif

et al. 2021

Int Trans Electr Energ Syst

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This manuscript proposes a modern optimization framework for parameter extraction of a triple-diode model of the unknown solar cell and Photovoltaic (PV) module parameters. The suggested optimization framework is based on applying a new metaheuristic optimization algorithm called Artificial Ecosystem-based Optimizer (AEO) to determine the nine unknown parameters of the triple-diode model of PV equivalent circuit model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. In this context, the root means squared error (RMSE)between the measured and estimated data is considered as the primary objective function. This objective function achieves the closeness degree between the estimated and experimental data. On the way to accomplish this study, the proposed AEO is carried out on three different commercial PV cells/modules.To assess the proposed algorithm, a comprehensive comparison study is used compared with several well-matured optimization algorithms reported in the literature. The attained numerical results prove the high precision and fast response of the proposed AEO algorithm for identifying multiple PV models.

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A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer

Cited by 101 publications

References 64 publications

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Parameter estimation of triple diode photovoltaic model using an artificial ecosystem‐based optimizer

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