Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms

Beigi, Amir Mohammad; Maroosi, Ali

doi:10.1016/j.solener.2018.06.092

Cited by 202 publications

(75 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These measurements are obtained under the following conditions 1 sun (1000 W/m 2 ) at 33 • C. For reasonable comparison, the search range of each parameter is given in Table 1. Additionally, the proposed algorithm was compared with four recent algorithms: (BHCS, 2019) [25], (ITLBO, 2019) [23], (ImCSA, 2018) [21] and (HFAPS, 2018) [16]. We consider the following ALO algorithm parameters such that population size Npop = 10 , the maximum number of iterations maxIt = 250 and 30 independent executions were performed to study the statistical performance of the proposed algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Because of these advantages, different meta-heuristic methods have been applied to solve PV parameter estimation problems. Such as particle swarm optimization (PSO) [6], simulated annealing algorithm (SA) [7], genetic algorithm (GA) [8], pattern search (PS) [9], biogeography based optimization (BBO) [10], Artificial bee colony (ABC) [11], chaotic asexual reproduction (CAR) [12], adaptive differential evolution (ADE) [13], symbiotic organic search (SOS) [14], improved shuffled complex evolution (ISCE) [15], hybrid firefly algorithm and patter search (HFAPS) [16], multi learning backtracking search (MLBTS) [17], firefly algorithm (FA) [18], ant lion optimization (ALO) [19,28], particle swarm optimization/ adaptive mutation strategy (PSOAMS) [20], improved cuckoo search algorithm (ImCSA) [21], Lambert W function [22], improved teaching learning based optimization (ITLBO) [23], adaptive differential evolution [24], hybridizing cuckoo search / biogiography based optimization (BHCS) [25] and three point based approach (TPBA) [26], exploiting intrinsic properties [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

Messaoud

2020

SN Appl. Sci.

View full text Add to dashboard Cite

This article addresses the problem of estimating the uncertain parameters of solar photovoltaic models using Ant Lion Optimization. The parameter extraction phase consists of three stages, the first stage is devoted to estimating the parameters without considering the uncertainties, the second stage deals with estimating the uncertainties of each parameter, with the aim of reducing of search interval for parameters to extract. Finally, the parameters are extracted instantaneously taking into account the results of the first two steps. For validation of proposed theoretical developments, the algorithm is applied to the academic example of the real data of silicon solar cell RTC France. The results obtained are compared with well-established algorithms. These comparisons have shown that the proposed method presents largely effective performances compared to existing methods in the literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

Messaoud

2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Recently, meta-heuristic approaches are developed to optimally attain the electrical parameters of different PV models of PV modules by minimizing the proposed cost functions. Genetic algorithm (GA) [18], hybrid trustregion-reflective algorithm [19], whale optimization algorithm (WOA) [7], improved WOA [20], improved chaotic WOA [21], hybrid firefly algorithm and pattern technology [22], and other heuristic algorithms [23]- [27] are widely applied to minimize the root mean square error and extract the PV model parameters. Moreover, a social network optimizer algorithm [28], a self-adaptive teachinglearning algorithm [29], and a multi-strategy successhistory-based adaptive differential evolution [30] are presented to identify the parameters of different PV models.…”

Section: Introductionmentioning

confidence: 99%

Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models

2020

View full text Add to dashboard Cite

The precise electrical modeling of photovoltaic (PV) module is crucial due to the large-scale permeation of PV power plants into electric power networks. Therefore, a triple-diode photovoltaic (TDPV) model is presented to address all PV losses. However, the TDPV is mathematically modelled by a nonlinear I-V behavior, including nine-parameters that cannot be directly determined from the PVs datasheet due to the lack data offered by the PV manufacturers. This article presents a new application of the marine predators algorithm (MPA) to properly extract the electrical parameters of the TDPV model of a PV panel. The validity of the MPA-based TDPV model is widely appraised by the numerical analyses, which are carried out under various temperatures and solar irradiations. The optimal nine-parameters achieved using the MPA are compared with that realized by different optimization approaches-based PV model. For a realistic study, the numerical results and the measured data are compared for the marketable Kyocera KC200GT and Solarex MSX-60 PV panels. The efficacy of the MPA-based TDPV model is properly executed by checking its current error with that obtained from various models. With the MPA technology, a highly accurate model of any marketable PV module can be attained, which represents a new contribution to the sector of PV power systems. INDEX TERMS Marine predators algorithm, photovoltaic modeling, photovoltaic power systems, solar energy, triple-diode model.

show abstract

“…Thus, dedicated efforts have been made to surmount the limitations of stochastic computational techniques through the establishment of alternative algorithms and internal parametric modifications. Examples of these approaches are PSO with binary constraints [38], guaranteed convergence PSO [39], improved chaotic whale optimization algorithm [40], improved shuffled complex evolution algorithm [37] and hybrid firefly algorithm with pattern search algorithm (HFAPS) [41]. Additionally, with the aim of combining the simplicity of analytical methods and efficiency of computational techniques various hybrid approaches have been introduced to estimate the PV cells and modules parameters [18, 21, 42, 43].…”

Section: Introductionmentioning

confidence: 99%

Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique

Muhammad

Sangawi²,

Hashim

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

The behavior of solar cells and modules under various operational conditions can be determined effectively when their intrinsic parameters are accurately estimated and used to simulate the current-voltage ( I-V ) characteristics. This work proposed a new computational approach based on approximation and correction technique (ACT) for simple and efficient extraction of solar cells and modules parameters from the single-diode model. In this technique, an approximated value of series resistance ( R s ) was first derived and used to determine the initial value of parallel resistance ( R p ). Later, the final corrected values of R s and R p were obtained by resubstituting their approximated values in a five-loop iteration using the manipulated equations. For rapid evaluation and validation of the proposed technique, a software application was also created using MATLAB program. The correctness and robustness of the proposed technique was validated on five types of solar cells and modules operated at varied temperatures and irradiances. The lowest RMSE value was achieved for RTC France (7.78937E-4) and PVM 752 GaAs (2.10497E-4) solar cell. The legitimacy of ACT extracted parameters was established using a simple yet competitive implementation approach wherein the performance of the developed technique was compared with several state-of-the-art methods recently reported in the literature.

show abstract

Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms

Cited by 202 publications

References 37 publications

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models

Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique

Contact Info

Product

Resources

About