Extraction of solar cell parameters from a single current–voltage characteristic using teaching learning based optimization algorithm

Patel, Sankita J.; Panchal, Ashish K.; Kheraj, Vipul

doi:10.1016/j.apenergy.2014.01.027

Cited by 128 publications

(53 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other evolutionary algorithms for extraction of parameters are bird mating algorithm [100], simplified BMA [101] which reduces the parameter setting in original BMA, artificial bee swarm optimization [102,103], frog leaping algorithm [104], harmony search method [105], fish swarm optimization [106], TLBO [107], simplified TLBO [108], biogeography based optimization [109] ( Table 6). …”

Section: Other Eamentioning

confidence: 99%

Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review

Jena

Ramana

2015

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Section: Other Eamentioning

confidence: 99%

Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review

Jena

Ramana

2015

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

“…In the TLBO process, the fitness of a learner is improved by the propagation of the knowledge through two phases, i.e., the "teacher phase" and the "learner phase," which are explained in detail elsewhere. 36 The power model coefficients are obtained by utilizing the set of (V, I) data of a PV cell or a module. In this work, the set of power model coefficients denoted by X i ¼ ða i ; b i ; c i Þ is defined as a learner in a classroom.…”

Section: Execution Of Tlbo Algorithm For Extraction Of the Powermentioning

confidence: 99%

“…Most importantly, the convergence to the final solution is almost independent of the initial population. This algorithm was successfully employed for the solar cell parameters extraction by Patel et al 36 …”

mentioning

confidence: 99%

Maximum power point computation using current–voltage data from open and short circuit regions of photovoltaic module: A teaching learning based optimization approach

Patel

Kumar

Panchal

et al. 2015

Journal of Renewable and Sustainable Energy

Self Cite

View full text Add to dashboard Cite

Articles you may be interested inAn intelligent control strategy of fractional short circuit current maximum power point tracking technique for photovoltaic applications J. Renewable Sustainable Energy 7, 013114 (2015); 10.1063/1.4906982Power quality assessment of a solar photovoltaic two-stage grid connected system: Using fuzzy and proportional integral controlled dynamic voltage restorer approach J. Renewable Sustainable Energy 7, 013113 (2015); 10.1063/1.4906980Modeling and global maximum power point tracking for photovoltaic system under partial shading conditions using modified particle swarm optimization algorithm J. Renewable Sustainable Energy 6, 063117 (2014); 10.1063/1.4904436 A maximum power point tracking algorithm for photovoltaic systems using bang-bang controller J. Renewable Sustainable Energy 6, 053126 (2014); 10.1063/1.4898358 Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: A particle swarm optimization technique Solar photovoltaic module operating with constant solar irradiance needs entire power (P)-voltage (V) curve tracing to find the maximum power point (MPP), if perturb and observe or incremental conductance algorithms are used. Therefore, there is a need of an algorithm which tracks the MPP without tracing entire P-V curve. This paper focuses on an MPP prediction method which is only based on a few data collected in the open-circuit and short-circuit regions of current (I)-voltage (V) curves of a cell or a module. The work presented here assumes I-V curve of a module to follow I-V power relation, I ¼ aV b þ c. The power model coefficients a, b, and c are determined by teaching-learning based optimization. The voltage at the MPP (V mp ) is computed by correlating the incremental conductance algorithm with the I-V power model and thereafter power at the MPP (P max ) is obtained. The effectiveness of the algorithm is confirmed by applying it to the reported I-V curve of different types of solar cells. The obtained values of V mp and P max are found to be in excellent agreement with their reported values. The algorithm is successfully applied to experimentally measured I-V curves of a silicon cell and a module. V C 2015 AIP Publishing LLC.

show abstract

“…The single diode model of solar cells is equivalent circuit based on the internal phy acting within solar cells [1][2][3][4][5][6][7][8]. The key param solar cell models behaviour are the genera saturation current, series resistance, shun ideality factor.…”

Section: Iintroductionmentioning

confidence: 99%

Solar cell parameters extraction using particle swarm optimization algorithm

Hamid

Rahim

Selvaraj

2013

2013 IEEE Conference on Clean Energy and Technology (CEAT)

View full text Add to dashboard Cite

This paper presents an application optimization (PSO) technique for extracting single diode solar cell model. The proposed te estimate five different model parameters; n photocurrent, saturation current, series resistance and ideality factor that govern t relationship of a solar cell. A measuremen diameter commercial (R.T.C. France) silicon s test and verify the consistency of accurately parameters. The effectiveness of the pro compared with the results found by the estimation techniques.IndexTerms-solar cell model, particle sw parameter extraction

show abstract

Extraction of solar cell parameters from a single current–voltage characteristic using teaching learning based optimization algorithm

Cited by 128 publications

References 27 publications

Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review

Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review

Maximum power point computation using current–voltage data from open and short circuit regions of photovoltaic module: A teaching learning based optimization approach

Solar cell parameters extraction using particle swarm optimization algorithm

Contact Info

Product

Resources

About