Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

IET Renewable Power Generation, Год журнала: 2024, Номер 18(6), С. 959 - 978

Опубликована: Фев. 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.

Язык: Английский

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Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 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

Язык: Английский

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Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 4, 2024

Abstract The growing demand for solar energy conversion underscores the need precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy PV system extraction, essential optimizing models under diverse environmental conditions. Utilizing primary (single diode, double and three diode) module models, research emphasizes importance of accurate identification. In response to limitations existing metaheuristic algorithms, introduces enhanced prairie dog optimizer (En-PDO). novel algorithm integrates strengths (PDO) with random learning logarithmic spiral search mechanisms. Evaluation against PDO, a comprehensive comparison eighteen recent spanning optimization techniques, highlight En-PDO’s exceptional performance across different cell CEC2020 functions. Application En-PDO single using experimental datasets (R.T.C. France silicon Photowatt-PWP201 cells) test functions, demonstrates its consistent superiority. achieves competitive or superior root mean square error values, showcasing efficacy accurately modeling behavior cells performing optimally These findings position as robust reliable approach estimation emphasizing potential advancements compared algorithms.

Язык: Английский

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A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models

Electrical Engineering, Год журнала: 2024, Номер 106(5), С. 6565 - 6585

Опубликована: Апрель 20, 2024

Язык: Английский

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Parameter extraction of photovoltaic cell models using electric eel foraging optimizer

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Авг. 1, 2024

Solar energy has emerged as a key solution in the global transition to renewable sources, driven by environmental concerns and climate change. This is largely due its cleanliness, availability, cost-effectiveness. The precise assessment of hidden factors within photovoltaic (PV) models critical for effectively exploiting potential these systems. study employs novel approach parameter estimation, utilizing electric eel foraging optimizer (EEFO), recently documented literature, address such engineering issues. EEFO emerges competitive metaheuristic methodology that plays crucial role enabling extraction. In order maintain scientific integrity fairness, utilizes RTC France solar cell benchmark case. We incorporate approach, together with Newton-Raphson method, into tuning process three PV models: single-diode, double-diode, three-diode models, using common experimental framework. selected because significant field. It serves reliable evaluation platform approach. conduct thorough statistical, convergence, elapsed time studies, demonstrating consistently achieves low RMSE values. indicates capable accurately estimating current-voltage characteristics. system’s smooth convergence behavior further reinforces efficacy. Comparing competing methodologies advantage optimizing model parameters, showcasing greatly enhance usage energy.

Язык: Английский

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Mitigating local minima in extracting optimal parameters for photovoltaic models: An optimizer leveraging multiple initial populations (OLMIP)

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 92, С. 367 - 391

Опубликована: Окт. 24, 2024

Язык: Английский

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I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem

Biomimetics, Год журнала: 2023, Номер 8(8), С. 569 - 569

Опубликована: Ноя. 27, 2023

The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based inspired by plants. In this study, the exploitation phase of CPA improved with teaching factor strategy in order to achieve balance between exploration and capabilities CPA, minimize getting stuck local minima, produce more stable results. called I-CPA. To test performance I-CPA, it applied CEC2017 functions. addition, I-CPA problem identifying optimum parameter values various solar photovoltaic modules, one real-world problems. According experimental results, best value root mean square error (RMSE) ratio standard data simulation obtained method. Friedman rank statistical analyses were also performed both As result analyses, observed that produced statistically significant results compared some classical modern metaheuristics. Thus, can be said achieves successful competitive parameters modules.

Язык: Английский

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Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124777 - 124777

Опубликована: Июль 14, 2024

Accurately estimating the unknown parameters of photovoltaic (PV) models based on measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution this essential for efficiently simulating, controlling, evaluating PV systems. There are three different models, including single-diode model, double-diode triple-diode with five, seven, nine parameters, respectively, proposed represent electrical characteristics systems varying levels complexity accuracy. In literature, several deterministic metaheuristic algorithms have been used accurately solve hard problem. However, problem, methods could not achieve solutions. On other side, algorithms, also known as gradient-free methods, somewhat good solutions but they still need further improvements strengthen their performance against stuck-in local optima slow convergence speed problems. Over last two years, recent better improve avoid tackle continuous majority those has investigated. Therefore, in paper, nineteen recently published such Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum (LSO), growth (GO), walrus (WAOA), hippopotamus (HOA), black-winged kite (BKA), quadratic interpolation (QIO), sinh cosh (SCHA), exponential distribution (EDO), optical microscope (OMA), secretary bird (SBOA), Parrot Optimizer (PO), Newton-Raphson-based (NRBO), crested porcupine (CPO), differentiated creative (DCS), propagation (PSA), one-to-one (OOBO), triangulation topology aggregation (TTAO), studied clarify effectiveness models. addition, collaborate functions, namely Lambert W-Function Newton-Raphson Method, aid solving I-V curve equations more accurately, thereby improving Those assessed using four well-known solar cells modules compared each metrics, best fitness, average worst standard deviation (SD), Friedman mean rank, speed; multiple-comparison test compare difference between ranks. Results comparison show that SWO efficient effective SDM, DDM, TDM over modules, Method equations. study reports perform poorly when applied

Язык: Английский

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Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions

Computer Science Review, Год журнала: 2024, Номер 53, С. 100647 - 100647

Опубликована: Июнь 7, 2024

Язык: Английский

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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2646 - e2646

Опубликована: Янв. 27, 2025

This study conducts a comparative analysis of the performance ten novel and well-performing metaheuristic algorithms for parameter estimation solar photovoltaic models. optimization problem involves accurately identifying parameters that reflect complex nonlinear behaviours cells affected by changing environmental conditions material inconsistencies. is challenging due to computational complexity risk errors, which can hinder reliable predictions. The evaluated include Crayfish Optimization Algorithm, Golf Coati Crested Porcupine Optimizer, Growth Artificial Protozoa Secretary Bird Mother Election Optimizer Technical Vocational Education Training-Based Optimizer. These are applied solve four well-established models: single-diode model, double-diode triple-diode different module focuses on key metrics such as execution time, number function evaluations, solution optimality. results reveal significant differences in efficiency accuracy algorithms, with some demonstrating superior specific Friedman test was utilized rank various revealing top performer across all considered optimizer achieved root mean square error 9.8602187789E-04 9.8248487610E-04 both models 1.2307306856E-02 model. consistent success indicates strong contender future enhancements aimed at further boosting its effectiveness. Its current suggests potential improvement, making it promising focus ongoing development efforts. findings contribute understanding applicability renewable energy systems, providing valuable insights optimizing

Язык: Английский

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