Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

Jiang, Lijing; Maskell, Douglas L.; Patra, J.C.

doi:10.1016/j.apenergy.2013.06.004

Cited by 260 publications

(89 citation statements)

References 38 publications

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“…Moreover, the proposed method rapidly converges and does not require complex computation. The proposed [39], IADE [40], and Rcr-IJADE [41], as well as the models presented by Ishaque et al [16] and Villalva et al [15].…”

Section: Introductionmentioning

confidence: 93%

“…Jiang et al [40] proposed a simple method to tune the control parameters of conventional DE. The proposed method [40] was based on the random weight exponent of the ratio of the current and previous best objective function values to adapt the control parameters within the range [0.5, 1].…”

Section: Proposed Strategy For Adjusting Mutation Factor and Crossovementioning

confidence: 99%

“…Since then, countless efforts have been exerted to improve conventional DE algorithms by proposing tuning strategies for mutation and crossover rates. An improved adaptive DE (IADE) algorithm was proposed by Jiang et al [40] to estimate the five parameters of a PV module model. The mutation factor and crossover rate were adjusted using a strategy based on exponential function.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm

Muhsen

Ghazali

Khatib³

et al. 2015

Energy Conversion and Management

141

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Section: Introductionmentioning

confidence: 93%

Section: Proposed Strategy For Adjusting Mutation Factor and Crossovementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm

Muhsen

Ghazali

Khatib³

et al. 2015

Energy Conversion and Management

141

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“…This case investigates the utilization of the proposed GBEST on the parameter extraction of multi-crystalline solar cell model [52] working under four different irradiance levels (G = Table 10. It can be observed that minimum RMSE value is attained by the GBEST method for each irradiance condition.…”

Section: Case Study 3: Multi-crystalline Solar Cell Modelmentioning

confidence: 99%

Global Best Algorithm Based Parameter Identification of Solar Cell Models

Turgut¹

2017

ijisae

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Effectivity of the solar energy systems is thoroughly dependent of successful modeling of the I-V characteristic curves. However, due to the lack of information about the precise model parameters those are profoundly involved in characterizing governing equations; an efficient design has not been accurately accomplished by researchers yet. This article proposes Global Best Algorithm (GBEST) in order to extract unknown parameters of solar cell models accurately. In order to test the performance of the proposed optimizer, nine different unconstrained optimization test functions are evaluated and their statistical results are compared with the recently developed metaheuristic algorithms. GBEST is applied on PV module, single and double diode models which are mathematically formulated as multi-dimensional and highly nonlinear in their nature. Results reveal that GBEST is superior to the other methods in terms of solution accuracy and efficiency.

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“…For example, application of genetic algorithm (GA) [11][12][13], particle swarm optimization (PSO) [14][15][16], simulated annealing (SA) [17], differential evolution (DE) [18][19][20][21][22], pattern search (PS) [23], harmony search (HS) [24], artificial bee swarm optimization (ABSO) [25], bird mating optimizer (BMO) [26], bacterial foraging optimization (BFO) [27], artificial bee colony (ABC) [28], biogeography-based optimization algorithm with mutation strategies (BBO-M) [29] and teaching-learning based optimization (TLBO) [30] for this purpose can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm

Jamadi

Merrikh-Bayat

Bigdeli

2015

Int J Energy Environ Eng

108

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This paper proposes an effective method for very accurate parameter estimation of single-and doublediode solar cell models. For this purpose, unknown parameters of model are estimated by minimization of an objective function using a new highly effective modified version of artificial bee colony (ABC) algorithm. The proposed algorithm is used for parameter estimation of single-and double-diode models and the results are compared, from different aspects, with recently developed related works (including those applied GA, CPSO, PS, SA, IGHS, ABSO, ABC, GGHS, and IADE for this purpose). The proposed algorithm is also compared with other modified explanations of ABC algorithm. The results show that the proposed strategy is considerably faster and more accurate compared to all the previous studies.

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Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

Cited by 260 publications

References 38 publications

Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm

Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm

Global Best Algorithm Based Parameter Identification of Solar Cell Models

Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm

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