Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

Li, Shuijia; Gong, Wenyin; Yan, Xuesong; Hu, Chengyu; Bai, Danyu; Wang, Ling; Gao, Liang

doi:10.1016/j.enconman.2019.02.048

Cited by 247 publications

(135 citation statements)

References 39 publications

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“…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%

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

Messaoud

2020

SN Appl. Sci.

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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.

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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.

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“…To validate the performance of SDO in solving the parameter extraction problem of PV models, SDO is applied to the following four different PV models with diverse characteristics: [30,49]:…”

Section: Resultsmentioning

confidence: 99%

“…composed of 36 cells in series with 20 pairs of I-V data points measured at 51°C (iv) STP6-120/36 polycrystalline module [30,49]:…”

Section: Resultsmentioning

confidence: 99%

“…Xiong et al solved the parameter extraction problem of different PV models by using several metaheuristics including symbiotic organisms search (SOS) algorithm [24], improved WOA based on two modified prey searching strategies [25], and hybrid DE with WOA [26]. In addition to the aforementioned metaheuristics, many more [27][28][29][30][31][32][33][34][35][36][37][38][39][40] have also been presented to solve the important problem. e abovementioned metaheuristics have, to some extent, proven themselves promising methods for the parameter extraction problem of PV models.…”

Section: Introductionmentioning

confidence: 99%

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Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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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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Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

Cited by 247 publications

References 39 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

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

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

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