PV cell and module efficient parameters estimation using Evaporation Rate based Water Cycle Algorithm

Kler, D. S.; Sharma, Pallavi; Banerjee, Ashish; Rana, K.P.S.; Kumar, Vineet

doi:10.1016/j.swevo.2017.02.005

Cited by 148 publications

(45 citation statements)

References 31 publications

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“…To show the additional advantage of COA over other techniques, we conducted a comparison in terms of required time for one iteration. In that sense, in MATLAB 2015 (MathWorks, Natick, MA, USA) we have implemented the following algorithms for solar cell parameter estimation: evaporation rate-based water cycle algorithm (ER-WCA) [19], cuckoo search (CS) [46] and harmony search (HS) [48]. ER-WCA algorithm has a very good accuracy, very close to that obtained by the proposed method (see Table 1).…”

Section: Simulation Resultsmentioning

confidence: 79%

“…It can be seen that COA outperforms several other techniques, such as evaporation rate-based water cycle algorithm (ER-WPA) [19] and cat swarm optimization (CSO) [24] for SDM, and with the generalized opposition-flower pollination algorithm-nelder-mead simplex method (GOFPANM) [53], by a small margin. However, the implementation of COA is simpler than implementation of ER-WPA, CSO and GOFPANM.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Note, in order to draw a fair comparison between the considered algorithms, MATLAB implementation follows the same rules for each algorithm (e.g., avoiding loops and using array operations such as dot product and matrix product whenever possible). [19] 0.021063 0.024145 0.019492 CS [46] 0.029179 0.037177 0.027130 HS [48] 0.021103 0.023264 0.020393…”

Section: Simulation Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

et al. 2019

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Estimation of single-diode and two-diode solar cell parameters by using chaotic optimization approach (COA) is addressed. The proposed approach is based on the use of experimentally determined current-voltage (I-V) characteristics. It outperforms a large number of other techniques in terms of average error between the measured and the estimated I-V values, as well as of time complexity. Implementation of the proposed approach on the I-V curves measured in laboratory environment for different values of solar irradiation and temperature prove its applicability in terms of accuracy, effectiveness and the ease of implementation for a wide range of practical environment conditions. The COA-based parameter estimation is, therefore, useful for PV power converter designers who require fast and accurate model for PV cell/module.

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Section: Simulation Resultsmentioning

confidence: 79%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

et al. 2019

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“…In order to simulate, manage and optimize the real solar systems, many optimization techniques dealt with the identification of PV cell parameters. Guaranteed convergence particle swarm optimization (GCPSO) [1], enhanced leader particle swarm optimization (ELPSO) [2], improved JAYA (IJAYA) [3], artificial bee colony (ABC) [4], particle swarm optimization (PSO) [5,6], genetic algorithms (GA) [7][8][9], evaporation rate based water cycle algorithm (ER-WCA) [10], simulated annealing (SA) [7], harmony search (HS) [11], teaching-learning based optimization (TBLO) [12], Bacterial Foraging Algorithm (BFA) [13], imperialist competitive algorithm (ICA) [14], self-adaptive teaching learning based optimization (SATLBO) [15], bird matting optimization (BMO) [16] and salp swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm

Nasri¹,

Mokeddem²,

Bourouba³

2020

IJIES

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Solar photovoltaic (PV) systems have recently attracted researcher’s attention as a clean source of energy. Thus, the importance to design appropriately the photovoltaic cells highly raises. The main problems faced in the design process are first, the development of a useful model describing the characteristics of the current vs. voltage able to simulate the real solar cells behaviours and then, the precise estimation of photovoltaic cells parameter values. This paper employs an improved version of Salp Swarm Algorithm called Chaotic Salp Swarm Algorithm (CSSA) for the parameters estimation of solar cells in both single and double diode models. CSSA approach benefits from chaotic maps proprieties, and has the advantage of providing good equilibrium between exploration and exploitation mechanisms as well. Performance of the proposed CSSA is compared to fourteen known algorithms. Experimental results demonstrate that the proposed algorithm has the ability to find the optimal solutions with an accurate estimation of parameters for the courant vs voltage characteristics of real solar cell with high performance.

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“…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%

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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PV cell and module efficient parameters estimation using Evaporation Rate based Water Cycle Algorithm

Cited by 148 publications

References 31 publications

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm

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

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