Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Tamrakar, Rituraj; Gupta, A.

doi:10.17148/ijireeice.2015.31116

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…In the resolution of this uncertainty, several computing techniques have been presented recently. Authors in [48, 49], presented adaptive differential evolution algorithm for the extraction of L5P using synthetic and the experimentally measured I-V curve. In [50], an analytical method for the parameters extraction based on the manufacturers data sheet only was presented.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Elkholy

El‐Ela

2019

Heliyon

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This paper introduces a proposed approach to estimate the optimal parameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear least-squares fitting algorithm. The main goal is to determine the optimal parameter values of the implemented model which are: series resistance, reverse saturation current, photocurrent, ideality factor and shunt resistance in case of the five parameters model. A Microsoft Excel spreadsheet is developed in order to perform modeling and analysis of the parameters analytical initial values using manufacturer datasheet specifications regarding to the changing in solar irradiance and ambient temperature. Then, the sum of the squared residuals between in-field measured and simulated data are calculated and minimized using Excel solver in order to obtain the optimal values of the parameters simultaneously, to describe the best fit for the outdoor measured data. The proposed approach is used to find the optimal parameters of the PV module TRINA TSM-295 using an array tester. The convergence confidences of the estimated parameters are presented and assessed in an easy way. This approach allows all parameters to be optimized, simultaneously. The results are verified and compared with other research studies for different PV cell technologies. The obtained results are useful for the tested PV module manufacturer and assess the performance of the products in different weather conditions.

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Section: Introductionmentioning

confidence: 99%

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Elkholy

El‐Ela

2019

Heliyon

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“…In the first stage of the parameter estimation, the five parameters defining the behavior of the curve in the first quadrant (I ph , R s , R sh , I o and η) were calculated. Thus, the ranges in Table 2 were selected as search ranges; those values were taken from the literature [41][42][43][44][45][46][47][48][49]. The photovoltaic current (I ph ) was adjusted to a range of ±5% of the short-circuit current of the test (I sc ), this based on the fact that I sc is caused by the generation and collection of light-generated carriers; therefore, I sc and I ph are very close values.…”

Section: Estimation Stages 421 Estimation Of the Sdm Parametersmentioning

confidence: 99%

Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm

et al. 2022

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Photovoltaic panels can be affected by partial shading, which causes some shaded cells to consume the energy generated by other cells of the panel. That is, shaded cells stop operating in the first quadrant and start operating in the second quadrant, with negative voltage at their terminals, causing power losses and other negative effects in the cells. The Bishop model is an accurate representation of the cells behavior at the second quadrant, but estimating its parameters is not a trivial task. Therefore, this paper presents a procedure to estimate the parameters of the Bishop model by using the Chu–Beasley optimization technique. The effectiveness of this procedure was evaluated using different accuracy measures such as RMSE and MAPE, obtaining values lower than 0.5%. In addition, the results of this study demonstrate that it is essential to estimate all the parameters of the Bishop model, illustrate the variation in the parameters according to the cell technology and show the strong influence of the shunt resistance into the behavior at the second quadrant.

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“…Phang in Reference [8] developed a novel analytical method for the rapid extraction of solar cell SDM parameters using experimental data. The resulting parameter values were found to significant improvements, with less than a 5% error for most of the solar cells studied [8][9][10][11][12][13]. The double diode model (DDM) illustrated in Figure 2 is also widely used by researchers due to its higher accuracy where more parameters are used in this model, and therefore, more calculations are performed [14][15][16][17][18][19][20][21][22].…”

Section: Main Idea Of the Discrete Symbiotic Organisms Search (Dsos)mentioning

confidence: 99%

A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

2019

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Exact models are a necessary prerequisite for optimal hardware configuration and the design of high-performance controllers. The photovoltaic system is considered a dynamic nonlinear multimodal system, where an optimization method must be used to resolve non-linearity and to identify the parameters describing the models of such systems. This has incited several researchers to work on and to develop several optimization methods. Recently, a number of methods have been proposed, including deterministic approaches, as well as probabilistic and stochastic numerical approaches, that aimed at obtaining a more accurate model for the PV cell and module array. This paper demonstrates the application of a performance optimization method based on discrete symbiotic organism search (DSOS), that mimics the behaviors of an organism in an ecosystem to survive. The high performance of such a method is attributable to the simplicity of the algorithm used; this algorithm is different from other heuristic algorithms, in that the GA needs two tuning parameters, i.e., the cross over and mutation rate, while the harmony search needs three rules to adjust and improvise new harmony, being memory consideration, pitch adjustment, and random choosing. Meanwhile, in the ABC algorithm, three phases are introduced to find the best food source, that is, the employed bee, the onlooker bee, and the scout bee phases, while the DSOS algorithm did not need any tuning parameters, wherein the proposed algorithm was used in both a single diode and double diode model across three test cases in the study. Compared to other previously published works, the level of performance of the algorithm is high in both implementation and accuracy; the DSOS algorithm is more capable of reaching the best set of solutions. The Mann-Whitney-Wilcoxon test to evaluate the discrete solutions of the algorithm for multiple runs with a 5% degree of confidence was evaluated and performed with a good level of accuracy.

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Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Cited by 6 publications

References 21 publications

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm

A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

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