Optimisation of solar photovoltaic (PV) parameters using meta-heuristics

Obiora, Valentine; Saha, Chinmoy; Al-Bazi, Ammar; Guha, Koushik

doi:10.1007/s00542-020-05066-3

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…PSO was initially successfully developed for continuous optimization issues [20][21]. A swarm in the fundamental model is made up of N particles that move about in a D-dimensional search space [23][24][25][26][27][28]. The vector xi in the decision space represents each particle i which is a potential answer to the conundrum.…”

Section: B Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method

Kanekar,

Burade,

Magare

2023

IJRITCC

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Industry and academia are becoming more interested in solar energy. The problem of providing an alternative to fossil fuels and limiting the environmental damage brought on by their emissions is what has brought about this increased focus. Solar photovoltaic is the subject of a growing number of studies. The goal of the current investigation is to investigate how various technological modules namely single junction amorphous silicon (a-Si), Hetero junction with Intrinsic Thin-layer (HIT) and multi crystalline silicon (mc-Si) are affected by seasonal spectrum variation respond to Indian climatic circumstances. Compared to many other nations, the entire Indian subcontinent has a relatively distinct climate with distinct seasonal patterns. The four seasons that are considered in this study are summer, winter, monsoon, and post monsoon. The impact of each season varies on the spectrum. Such a study will be helpful to measure the parameter connected to the spectrum and assess its impact on the effectiveness of the PV array. In order to conduct accurate performance investigations, the extraction of the right circuit model parameters is essential. The estimation of the solar PV parameter is done by using Particle swarm optimization (PSO) algorithm.

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Section: B Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method

Kanekar,

Burade,

Magare

2023

IJRITCC

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“…SA is a greedy algorithm based on the annealing of solids in physics, which eliminates the non-uniform state in the physical system by using the processes of heating and isothermal cooling. The heating process is the parameter initialization [31], the isothermal process corresponds to Metropolis sampling, and the cooling process corresponds to the decrease in parameters. Metropolis criterion is the core of SA algorithm, which receives deteriorating solutions with a certain probability p. The climbing speed and global searching speed of GA are enhanced by performing annealing operations on individuals with higher fitness value.…”

Section: Adaptive Ga and Sa Algorithmmentioning

confidence: 99%

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

et al. 2022

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The P–U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP voltage of PV cells. Finally, the fuzzy logical control (FLC) is used to eliminate local power oscillation and improve the robustness of the PV system. The proposed algorithm is applied and compared with GA-BPNN, simulated annealing-genetic (SA-GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and FLC algorithm under the condition that both the irradiance and temperature change. Simulation results indicate that the proposed MPPT algorithm is superior to the above-mentioned algorithms with efficiency, steady-state oscillation rate, tracking time and stability accuracy, and they have a good universality and robustness.

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“…El Hami et al, 2010;Reddad et al, 2022;Rhouas & El Hami, 2022;Zemzami et al, 2017Zemzami et al, , 2020, in diverse fields such as engineering design (Chien et al, 2021;Granados-Rojas et al, 2021;N. El Hami et al, 2015a, 2015bKumar et al, 2020;Ranjan et al, 2022), logistics, supply chain management, finance (N. El Hami & Bouchekourte, 2016), manufacturing and production planning, telecommunications (Ammari et al, 2014;Ghallali et al, 2013), healthcare (Alrajeh et al, 2012, environmental control and protection (Bhanja et al, 2022), energy systems (Lailianfeng & ChangTing-cheng, 2021;Obiora et al, 2021), transportation and logistics, machine learning and data mining, healthcare and water resource management. Metaheuristic optimization algorithms are a class of search algorithms designed to find near-optimal solutions to complex optimization problems (Hakima et al, 2022;Reddad et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

Reddad,

Zemzami,

Hami

et al. 2024

Preprint

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Metaheuristic optimization algorithms are powerful for solving complex optimization problems, almost when we lack information about the nature of their objective function. Meanwhile, the Finite Element Method (FEM) is a very powerful method for predicting and simulating the behavior of systems subjected to various stresses, whether electrical, thermal, mechanical, vibratory or magnetic. In the current study, we have proposed an optimization methodology that couples a metaheuristic algorithm - the Search and Rescue Algorithm (SAR) - with the finite element method. We then investigated the proposed methodology for optimizing the reliability of solder balls with respect to their geometry in a Ball Grid Array (BGA) assembly subjected to thermal cycling load. First, we performed a sensitivity analysis to determine the parameters influencing solder ball reliability, then we optimized the plastic deformation of solder balls, hence increasing their reliability as a function of their geometry through the proposed methodology, which results in improving the reliability of the entire electronic component.

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Optimisation of solar photovoltaic (PV) parameters using meta-heuristics

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 10 publications

References 26 publications

Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method

Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

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