Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Bechouat, Mohcene; Younsi, Abdelaziz; Sedraoui, Moussa; Soufi, Youcef; Yousfi, Laatra; Tabet, Ismail; Touafek, K.

doi:10.1007/s40095-017-0252-6

Cited by 26 publications

(8 citation statements)

References 23 publications

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“…The parameters of the PV-module model can be defined using a strategy of estimation (or identification) to minimize a defined criterion in which the PV-model characteristics are close to the real system characteristics given in the datasheet [51,[53][54][55][56].…”

Section: Modeling Of the Pv-modulementioning

confidence: 99%

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

et al. 2019

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In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.

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Section: Modeling Of the Pv-modulementioning

confidence: 99%

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

et al. 2019

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“…Compared to previously-used metaheuristic techniques, the firefly algorithm achieved reliable and valid results in tuning the photovoltaic parameters. The efficiency of the PSO and genetic algorithm for a similar objective was demonstrated by Bechouat, et al [111]. Wind driven optimization was successfully used by Abdalla, et al [112] to dealing with the optimal power tracking of photovoltaics systems.…”

Section: Introductionmentioning

confidence: 95%

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Moayedi¹,

Mosavi²

2021

Preprint

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Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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“…Compared to previously used metaheuristic techniques, the firefly algorithm achieved reliable and valid results in tuning the photovoltaic parameters. The efficiency of the PSO and genetic algorithm (GA) for a similar objective was demonstrated by Bechouat, et al [146]. Wind-driven optimization was successfully used by Abdalla, et al [147] to deal with the optimal power tracking of photovoltaic systems.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Moayedi

Mosavi

2021

Energies

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Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

show abstract

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Cited by 26 publications

References 23 publications

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

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