Bacterial Foraging Algorithm based solar PV parameter estimation

Rajasekar, N.; Kumar, Neeraja Krishna; Venugopalan, Rini

doi:10.1016/j.solener.2013.08.019

Cited by 276 publications

(102 citation statements)

References 24 publications

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“…In recent years-due to their notable potentials, soft computing methods such as the genetic algorithm [74][75][76][77], differential evolution [78][79][80], particle swarm optimization [81][82][83][84], simulated annealing [73], cuckoo search [85], bacteria foraging algorithm [86], harmony search [87], pattern search optimization [88,89], artificial bee swarm optimization [90] and the bird mating optimizer [91] have been employed to solve for the model parameter by extraction. They are particularly attractive due to their global q Current for other than STC is performed using the equation given as Translational equations:…”

Section: Extraction Of Parameters By Curve-fitting/optimizationmentioning

confidence: 99%

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Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

2015

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Section: Extraction Of Parameters By Curve-fitting/optimizationmentioning

confidence: 99%

“…Rajasekar et al [86] developed a BFA modelling to compute all parameters of the single diode R P -model at different operating G and T. By utilising only information available on the manufacturer's datasheet, I PV and I 0 were analytically computed, while a, R s , and R p were obtained by optimising the slope equation at MPP.…”

Section: Pattern Search (Ps)mentioning

confidence: 99%

Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

2015

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“…In order to explore separately the local and global search proprieties of BFOA, researchers hybridized it with other algorithms, applied it to several real world problems and proved its effectiveness over many works, e.g., mathematical modeling for solving simultaneous equation problems [19], solving scheduling problems [12], mechanical problems [2], solar PV modeling [20], and numerical optimization [1]. More hybridization is observed in [18,21].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

2017

J Inf Process Syst

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Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

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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

105

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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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Bacterial Foraging Algorithm based solar PV parameter estimation

Cited by 276 publications

References 24 publications

Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

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

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