Evaluation of Weighted Mean of Vectors Algorithm for Identification of Solar Cell Parameters

Hassan, Amir Y.; Ismaeel, Alaa A. K.; Said, Mokhtar; Ghoniem, Rania M.; Deb, Sanchari; El-Sayed, Abeer Galal

doi:10.3390/pr10061072

Cited by 16 publications

(5 citation statements)

References 52 publications

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“…Therefore, efforts were focused on using hybrid renewable energy systems (HRES), which use clean renewable energy sources like solar electricity, wind, biomass, hydropower, and fuel cells, to increase the stability of the power supply. Moreover, HRESs can be used to bridge the gap between supply and demand loads or to send electricity to remote places due to the rapidly increasing need for power and the incapacity of conventional plants to meet it [9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Said,

Gad,

El-Gaafary

et al. 2024

Preprint

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An optimum design of Photovoltaic-wind turbine-fuel cell hybrid energy systems (HRES) linked to a stand-alone micro-grid to meet the energy requirements of industrial and residential buildings in the Egyptian city of Siwa with anew algorithm introduced in this paper. The main photovoltaic (PV) and wind turbine (WT) hybrid renewable energy systems (HRESs) provide an affordable means of distributing power in these areas and then there is surplus power, it is efficiently channeled towards the electrolyzer to produce hydrogen. When the generated power cannot provide sufficient power, stored hydrogen is furnished to a proton exchange membrane fuel cell which, smoothly and without interruption, supplies the load. Fuel cells serve as supplemental sources intended to balance out power fluctuations and provide a steady supply of electricity to the load. The Zebra algorithm (ZOA) is used to determine the hybrid system's ideal size and compare results with Cuckoo Search algorithm (CSA). To match the load of the site with high operational reliability, a fitness function is employed loss of power supply probability (LPSP). To have a high-performance, dependable system, the optimization problem's choice variables such as the quantity of PV arrays and WTs are optimized. Based on the findings, 150 FCs, 113 wind turbines, and 82 PV arrays make up the ideal HRES system. The optimization performance has been excellent, even when the unmet load is zero with lowest COE.

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Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Said,

Gad,

El-Gaafary

et al. 2024

Preprint

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“…MHAs also achieve high solution accuracy and competitive computational efficiency compared to traditional methods when tackling complex problems [20]. Representative approaches are differential evolution (DE) [21,22], particle swarm optimization (PSO) [23,24], gaining-sharing knowledge algorithm (GSK) [20,25], whale optimization algorithm (WOA) [26,27], genetic algorithm (GA) [28], artificial bee colony optimization (ABC) [29,30], Grey-wolf optimization [31], and JAYA optimization [32,33], among many advanced algorithms [34][35][36][37][38]. Many MHAs have obtained excellent performances for the parameter estimation of PV models.…”

Section: Introductionmentioning

confidence: 99%

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Yuan,

Ji,

Chen

et al. 2023

Sustainability

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Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10−4, 9.82484851784979 × 10−4, 9.82484851784993 × 10−4, 2.42507486809489 × 10−3, 1.72981370994064 × 10−3, and 1.66006031250846 × 10−2. Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification.

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“…The goal of this research is to provide an effective search technique for estimating the un-known parameters of PV cells and discussing them through a recent optimizer (INFO). The presented different models of PV systems such as static models (SDM, and DDM) and different dynamic models (IOM, and FOM) that are not discussed in other references such as [23]. The INFO algorithm has strong performance in parameter optimization, which has been proven in [24].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Elnagi¹,

Kamel²,

Ramadan³

et al. 2023

Computers, Materials &Amp; Continua

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Renewable energy sources are gaining popularity, particularly photovoltaic energy as a clean energy source. This is evident in the advancement of scientific research aimed at improving solar cell performance. Due to the non-linear nature of the photovoltaic cell, modeling solar cells and extracting their parameters is one of the most important challenges in this discipline. As a result, the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate. In this paper, a weIghted meaN oF vectOrs algorithm (INFO) that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way. In each generation, the INFO utilizes three operations to update the vectors' locations: updating rules, vector merging, and local search. The INFO is applied to estimate the parameters of static models such as single and double diodes, as well as dynamic models such as integral and fractional models. The outcomes of all applications are examined and compared to several recent algorithms. As well as the results are evaluated through statistical analysis. The results analyzed supported the proposed algorithm's efficiency, accuracy, and durability when compared to recent optimization algorithms.

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Evaluation of Weighted Mean of Vectors Algorithm for Identification of Solar Cell Parameters

Cited by 16 publications

References 52 publications

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

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