Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells DOI Creative Commons
Wafaa Alsaggaf, Mona Gamal, Shahenda Sarhan

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(18), P. 8549 - 8549

Published: Sept. 23, 2024

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing energy conversion efficiency, a significant barrier to the widespread adoption energy. Accurate modeling estimation PV parameters are essential optimal design, control, simulation systems. Traditional methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces Material Generation Algorithm (MGA), inspired by principles material chemistry, estimate effectively. MGA simulates creation stabilization chemical compounds explore optimize parameter space. algorithm mimics formation ionic covalent bonds generate new candidate solutions assesses their stability ensure convergence parameters. applied two different modules, RTC France Kyocera KC200GT, considering manufacturing technologies cell models. nature comparison other algorithms further demonstrated experimental statistical findings. A comparative analysis results indicates that outperforms strategies previous researchers have examined systems terms both effectiveness robustness. Moreover, demonstrate enhances electrical properties accurately identifying under varying operating conditions temperature irradiance. In reported methods, KC200GT module, consistently performs better decreasing RMSE across variety weather situations; SD DD models, percentage improvements vary 8.07% 90.29%.

Language: Английский

Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm DOI
Mahmoud A. El‐Dabah, Ragab A. El‐Sehiemy, Hany M. Hasanien

et al.

Energy, Journal Year: 2022, Volume and Issue: 262, P. 125522 - 125522

Published: Sept. 23, 2022

Language: Английский

Citations

101
Parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution DOI
Amr A. Abd El-Mageed, Amr A. Abohany, Hatem M. H. Saad

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 134, P. 110032 - 110032

Published: Jan. 14, 2023

Language: Английский

Citations

61
L-SHADE with parameter decomposition for photovoltaic modules parameter identification under different temperature and irradiance DOI
Qiong Gu, Shuijia Li, Wenyin Gong

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 143, P. 110386 - 110386

Published: May 10, 2023

Language: Английский

Citations

53
Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules DOI Open Access
Houssem Ben Aribia, Ali M. El‐Rifaie, Mohamed A. Tolba

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(10), P. 7896 - 7896

Published: May 11, 2023

One of the most significant barriers to broadening use solar energy is low conversion efficiency, which necessitates development novel techniques enhance equipment design. The correct modeling and estimation cell parameters are critical for control, design, simulation PV panels achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity become caught in some local optima. In study, Growth Optimization (GO) algorithm developed simulated from humans’ learning reflection capacities social growing activities. It based on mimicking two stages. First, procedure through people mature by absorbing information others. Second, examining one’s weaknesses altering aid improvement. estimating different modules, RTC France Kyocera KC200GT manufacturing technology modeling. Three present-day contrasted GO’s performance valley optimizer (EVO), Five Phases Algorithm (FPA), Hazelnut tree search (HTS) algorithm. results electrical properties systems due implemented GO technique. Additionally, technique can determine unexplained considering diverse operating settings varying temperatures irradiances. For module, achieves improvements 19.51%, 1.6%, 0.74% compared EVO, FPA, HTS PVSD 51.92%, 4.06%, 8.33% PVDD, respectively. proposed 94.71%, 12.36%, 58.02% 96.97%, 5.66%, 61.20%

Language: Английский

Citations

49
Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm DOI Creative Commons

Nandhini Kullampalayam Murugaiyan,

C. Kumar,

M. Premkumar

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 4, 2024

Abstract Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by propensity conventional algorithms to get trapped in local optima due complex nature Accurate estimation, nonetheless, crucial its significant impact on PV system’s performance, influencing both current energy production. While traditional methods have provided reasonable results for model variables, they often require extensive computational resources, which impacts precision robustness many fitness evaluations. To address this problem, paper an improved algorithm extraction, leveraging opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with learning, provides enhanced exploration capability efficient exploitation search space, helping mitigate risk entrapment optima. proposed rigorously verified against state-of-the-art across various including single-diode, double-diode, three-diode, module models. Practical statistical reveal that performs better than other estimating parameters, demonstrating superior convergence speed, reliability, accuracy. Moreover, performance assessed using several case studies, further reinforcing effectiveness. Therefore, OBEDO, advantages terms efficiency robustness, emerges as promising solution identification, making contribution enhancing systems.

Language: Английский

Citations

27
Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems DOI Creative Commons
Serdar Ekinci, Davut İzci, Abdelazim G. Hussien

et al.

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: 18(6), P. 959 - 978

Published: Feb. 20, 2024

Abstract The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial maximizing conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm proposed and evaluated. GOANM synergistically integrates the gazelle optimization (GOA) with Nelder–Mead (NM) algorithm, offering an efficient powerful approach parameter extraction PV models. This investigation involves thorough assessment of algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional CEC2020 functions. Notably, consistently outperforms other approaches, demonstrating enhanced convergence speed, accuracy, reliability. Furthermore, application extended to single diode double models RTC France cell model Photowatt‐PWP201 module. experimental results demonstrate that approaches terms accurate estimation, low root mean square values, fast convergence, alignment data. These emphasize its role achieving superior efficiency renewable systems.

Language: Английский

Citations

24
Parameter estimation of various PV cells and modules using an improved simultaneous heat transfer search algorithm DOI
Xu Chen, Shuai Wang, Kaixun He

et al.

Journal of Computational Electronics, Journal Year: 2024, Volume and Issue: 23(3), P. 584 - 599

Published: March 29, 2024

Language: Английский

Citations

17
Self-adaptive enhanced learning differential evolution with surprisingly efficient decomposition approach for parameter identification of photovoltaic models DOI
Yu-Jun Zhang, Shuijia Li, Yufei Wang

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 308, P. 118387 - 118387

Published: April 5, 2024

Language: Английский

Citations

17
Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023) DOI Creative Commons

Eghbal Hosseini,

Abbas M. Al-Ghaili, Dler Hussein Kadir

et al.

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 53, P. 101409 - 101409

Published: May 1, 2024

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling forecasting tasks. While excels in capturing intricate patterns data, it may falter achieving optimality due to nonlinear nature energy data. Conversely, offer optimization capabilities but suffer from computational burdens, especially with high-dimensional This paper provides comprehensive review spanning 2018 2023, examining integration within frameworks applications. We analyze state-of-the-art techniques, innovations, recent advancements, identifying open research challenges. Additionally, we propose novel framework that seamlessly merges into paradigms, aiming enhance performance efficiency addressing problems. contributions include: 1. Overview advancements MHs, DL, integration. 2. Coverage trends 2023. 3. Introduction Alpha metric evaluation. 4. Innovative harmonizing MHs DL

Language: Английский

Citations

17
Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy DOI
X.J. RU

Solar Energy, Journal Year: 2024, Volume and Issue: 269, P. 112353 - 112353

Published: Jan. 20, 2024

Language: Английский

Citations

16