A New Parameter Estimation Method of Solar Photovoltaic

Jadli, Utkarsh; Thakur, Padmanabh; Shukla, Rishabh Dev

doi:10.1109/jphotov.2017.2767602

Cited by 80 publications

(69 citation statements)

References 27 publications

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“…Rearranging and taking log on both sides, we get (5) Again rearranging, we get (6) Open circuit voltage or Maximum Voltage (I L =0) is given by (7) Relation between V L and I L seen from load (considering shunt resistance) is given as follows (8)…”

Section: Fig1 Equivalent Circuit Of Solar Cellmentioning

confidence: 99%

Particle Swarm Optimization Technique for Photovoltaic System

Ansari*,

Thakur,

Saini

2020

IJRTE

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Day by day the dependency on renewable energy uses has been increasing because of no greenhouse emission and abundant in nature available freely, this paper, presents a comparative analysis of an optimization technique called Particle Swarm Optimization (PSO) along with Perturb & Observe (P&O) for the extraction of maximum power from the PV panel. The performances of P&O and PSO techniques were compared for different insolations and temperatures. A detailed and rigorous mathematical model along with simulation results and its performance for maximum power extraction from the panel were analyzed by using P&O and PSO. It has been observed that the maximum power obtained from PSO model is more than the maximum power obtained from P&O for different insolations and temperatures. Thus PSO is much better and more suitable for extracting maximum power from PV system.

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Section: Fig1 Equivalent Circuit Of Solar Cellmentioning

confidence: 99%

Particle Swarm Optimization Technique for Photovoltaic System

Ansari*,

Thakur,

Saini

2020

IJRTE

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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 new candidate solution X new for the paired X i and X j , is calculated based on the mutualistic symbiosis for both X i and X j as follows: Equations (12) and (13).…”

Section: Mutualismmentioning

confidence: 99%

“…To identify the intrinsic parameter of the vectors ϕ = I 0 , I ph , n, R s , R sh for the SDM, it is described using Equation (1) or ϕ = I 01 , I 02 , I ph , n 1 , n 2 , R s , R sh For the DDM, it was described using Equation 13, and we fitted the best model described by either Equation (1) or Equation (13) to the experimental data V = g(I). Through minimization of the squared error of the theoretical and experimental curves, we defined an objective function (OF) that evaluated the degree of correspondence between the estimated and calculated parameters as a sum of the squared errors (SSE), which is given by:…”

Section: Parameter Optimization Problem For Sdm and Ddmmentioning

confidence: 99%

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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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“…Numerical extraction methods are based on curve fitting by using some information in the manufacturer data-sheet. However, curve fitting of nonlinear PV characteristic suffers from complexity in mathematical derivation, need initial conditions and required certain assumptions [21] Meta-heuristic optimization algorithms including (evolutionary algorithms, swarm based algorithms and physical based algorithms) have attracted much attention in recent research to deal with high nonlinear and nonconvex problems without involving excessive arithmetic computations in order to give better accuracy and reliability [22]. These approaches are populistic and population based and inspired from nature that efficient in solving engineering optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Parameters estimation of solar photovoltaic module using camel behavior search algorithm

Hassan

Rashid

Jasim

2021

IJECE

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Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.

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A New Parameter Estimation Method of Solar Photovoltaic

Cited by 80 publications

References 27 publications

Particle Swarm Optimization Technique for Photovoltaic System

Particle Swarm Optimization Technique for Photovoltaic System

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

Parameters estimation of solar photovoltaic module using camel behavior search algorithm

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