Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators

Computers & Electrical Engineering, Journal Year: 2023, Volume and Issue: 106, P. 108603 - 108603

Published: Jan. 23, 2023

Language: Английский

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Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules

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

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

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

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Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules

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

Published: Feb. 11, 2024

Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this due their potential approximate the optimal solution for several complicated problems. Despite that, existing still suffer from sluggish convergence rates and stagnation in local optima when applied tackle problem. study presents new estimation technique, namely HKOA, based on integrating published Kepler algorithm (KOA) with ranking-based update exploitation improvement mechanisms estimate unknown parameters third-, single-, double-diode models. former mechanism aims at promoting KOA’s exploration operator diminish getting stuck optima, while latter strengthen its faster converge solution. Both KOA HKOA are validated using RTC France solar cell five PV modules, including Photowatt-PWP201, Ultra 85-P, STP6-120/36, STM6-40/36, show efficiency stability. In addition, they extensively compared techniques effectiveness. According experimental findings, strong alternative method estimating because it can yield substantially different superior findings

Language: Английский

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Wind power forecasting system with data enhancement and algorithm improvement

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 196, P. 114349 - 114349

Published: March 1, 2024

Language: Английский

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Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

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

Published: Jan. 20, 2024

Language: Английский

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Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review

Sustainability, Journal Year: 2023, Volume and Issue: 15(4), P. 3312 - 3312

Published: Feb. 10, 2023

As the photovoltaic (PV) market share continues to increase, accurate PV modeling will have a massive impact on future energy landscape. Therefore, it is imperative convert difficult-to-understand systems into understandable mathematical models through equivalent models. However, multi-peaked, non-linear, and strongly coupled characteristics of make challenging extract parameters Metaheuristics can address these challenges effectively regardless gradients function forms, gained increasing attention in solving this issue. This review surveys different metaheuristics model parameter extraction explains multiple algorithms’ behavior. Some frequently used performance indicators measure effectiveness, robustness, accuracy, competitiveness, resources consumed are tabulated compared, then merits demerits algorithms outlined. The patterns variation results extracted from external environments were analyzed, corresponding literature was summarized. Then, for both application scenarios analyzed. Finally, perspectives research summarized as valid reference technological advances extraction.

Language: Английский

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Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(2), P. 112210 - 112210

Published: Feb. 14, 2024

Language: Английский

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Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 1422 - 1445

Published: Jan. 9, 2024

Abstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics PV cells. The goal this paper propose multi strategy sine–cosine algorithm (SCA), named enhanced (ESCA), evaluate nondirectly measurable parameters ESCA introduces concept population average position increase exploration ability, at same time personal destination agent mutation mechanism competitive selection into SCA provide more search directions for while ensuring accuracy diversity maintenance. To prove that proposed best choice extracting cells, evaluated by single‐diode model, double‐diode three‐diode module model (PVM), compared with eight existing popular methods. Experimental results show outperforms similar methods in terms maintenance, high efficiency, stability. In particular, method less than 0.081, 0.144, 0.578 standard deviation statistics metrics three PVM models (PV‐PWP201, STM6‐40/36, STP6‐120/36), respectively. Therefore, an accurate reliable

Language: Английский

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A novel hybrid swarm intelligence algorithm for solving TSP and desired-path-based online obstacle avoidance strategy for AUV

Robotics and Autonomous Systems, Journal Year: 2024, Volume and Issue: 177, P. 104678 - 104678

Published: March 1, 2024

Language: Английский

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