A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization

Nunes, Hugo; Pombo, J.A.N.; Mariano, S.J.P.S.; Calado, M.R.A.; Souza, J. A. M. Felippe de

doi:10.1016/j.apenergy.2017.11.078

Cited by 209 publications

(125 citation statements)

References 74 publications

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“…The third case refers to polycrystalline SW255. The fourth case implements real experimental data from the Sharp ND-R250A5 PV module provided to us by [11].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This double-diode model ( Figure 2) has a better accuracy than one diode model, but also more complex because of the numbers of parameters [11]. The model has been used by many authors [11] [43] [44] [45].…”

Section: Double Diode Modelmentioning

confidence: 99%

“…The typical electrical characteristics of the PV cell at STC are listed in Table 6; the lower and upper bound are expressed in Table 7. The 26 I-V measured data have been collected from [11]. The initial parameters of EVPS are the same as in case study 1.…”

Section: Case Studymentioning

confidence: 99%

“…As output power is proportional to solar irradiance, an estimate of the intrinsic parameters of the PV is necessary in order to evaluate its performance [10]. To extract these intrinsic parameters, we can use either the manufacturer's datasheet or experimentally measure the voltage and current from the PV [11].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the efficiency of the numerical methods, their slow convergence does not always guarantee the best result because they can converge through a local minimum and the choice of the initial condition is not often easy [11].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

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To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.weather conditions change, analytical methods become ineffective [18].Numerical extraction techniques based on some algorithm fit the points on the PV characteristic curve. Compared to the analytical method, an accurate result can be attained since the algorithm tries to consider all points on the characteristic curve [19]. In the literature, the Newton-Raphson method is the most used [19] [20] [21]; In [22], a numerical method is proposed for modeling and the simulation of PV. The method finds the five parameters from the current-voltage characteristic by using three points of the curve (maximum power, open circuit and short circuit). In [23], the Levenberg Marquardt algorithm is

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“…The third case refers to polycrystalline SW255. The fourth case implements real experimental data from the Sharp ND-R250A5 PV module provided to us by [11].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Double Diode Modelmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

View full text Add to dashboard Cite

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A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Chauhan

Prakash

2021

Int Trans Electr Energ Syst

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Summary Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation‐ (EPO) based parameter estimation approach for a single‐diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current‐voltage (I‐V) and power‐voltage (P‐V) of KC200GT, PWP201, and STP6 120‐36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem.

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Review on parameter estimation techniques of solar photovoltaic systems

Venkateswari

Rajasekar

2021

Int Trans Electr Energ Syst

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Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta-heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation.But existing reviews focused on comparative analysis of analytical and metaheuristic approaches, analysis of models, and application of meta-heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on

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A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization

Cited by 209 publications

References 74 publications

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Review on parameter estimation techniques of solar photovoltaic systems

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