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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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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Special Trans Function based exact expressions for the double and triple diode models of solar cells: Superior fitness, accuracy and convergence

Energy Reports, Год журнала: 2024, Номер 11, С. 5252 - 5270

Опубликована: Май 15, 2024

Inspired by the fact that Special Trans Function (STF) is more accurate than Lambert W function (LWF), this paper rigorously derives two types of STF-based exact expressions for double-diode model (DDM) and triple-diode (TDM) solar cells. The former involves multi-STF (mSTF), while latter contains single-STF (sSTF), bridging remaining gap in describing nonlinear I–V characteristic cells using STF. proposed mSTF sSTF are closely linked with but different from multi-Lambert (mLWF), single-LWF (sLWF) implicit exponential (IEF) based expressions, particularly terms fitness parameter extraction. Through a test involving 203 cases space STF branch x∈R+, it was found under same values, consistently outperforms fitting measured data various cell/modules, followed sSTF, mLWF, sLWF, finally IEF-based expression. Notably, all optimal x fall within interval x∈[0,6] rather preset x∈[0,20]. A normalized trust-region-reflective (NTRR) algorithm V-shaped selection strategy developed to improve extraction sSTF. Results derivative-dependent NTRR population-based Rcr-IJADE indicate achieves highest accuracy fastest convergence speed, mSTF, mLWF Moreover, exhibits fewer degeneracies TDM DDM compared other expressions. Given these advantages, show great promise PV simulation therefore deserve serious attention.

Язык: Английский

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A systematic review on software reliability prediction via swarm intelligence algorithms

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(7), С. 102132 - 102132

Опубликована: Июль 20, 2024

The widespread integration of software into all parts our lives has led to the need for higher reliability. Ensuring reliable usually necessitates some form formal methods in early stages development process which requires strenuous effort. Hence, researchers field reliability introduced Software Reliability Growth Models (SRGMs) as a relatively inexpensive approach prediction. Conventional parameter estimation SRGMs were ineffective and left more be desired. Consequently, sought out swarm intelligence combat its flaws, resulting significant improvements. While similar surveys exist within domain, are broader scope do not cover many algorithms. Moreover, resulted occasional omission information regarding design predictions. A comprehensive survey containing 38 studies 18 different algorithms domain is presented. Each proposed by was systematically analyzed where relevant including measures used, datasets effectiveness each design, extracted organized tables taxonomies able identify current trends domain. Some notable findings include distance-based providing high prediction accuracy an increasing trend hybridized variants designs predict Future encouraged Mean Square Error (MSE) or Root MSE offer largest sample size comparison.

Язык: Английский

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An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

Artificial Intelligence Review, Год журнала: 2024, Номер 57(9)

Опубликована: Авг. 12, 2024

Abstract A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from behavior in world. To emulate impact gorillas at each stage search process, employs flexible weighting mechanism rooted concept. exceptional qualities, including independence derivatives, lack parameters, user-friendliness, adaptability, simplicity, have resulted rapid adoption addressing various optimization challenges. This review dedicated examination discussion foundational research that forms basis GTO. It delves into evolution this algorithm, drawing insights 112 studies highlight Additionally, it explores proposed enhancements GTO’s behavior, with specific focus on aligning geometry area real-world problems. also introduces solver, providing details about identification organization, demonstrates application scenarios. Furthermore, provides critical assessment convergence while limitation In conclusion, summarizes key findings study suggests potential avenues future advancements adaptations related

Язык: Английский

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Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model

Forecasting, Год журнала: 2024, Номер 6(2), С. 357 - 377

Опубликована: Май 22, 2024

This study introduces a novel adjustment to the firefly algorithm (FA) through integration of rare instances cannibalism among fireflies, culminating in development honeybee mating-based (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served as rigorous testing ground evaluate efficacy new diverse optimization scenarios. Moreover, thorough statistical analyses, including two-sample t-tests and fitness function evaluation analysis, algorithm’s capabilities were robustly validated. Additionally, coefficient determination, used an objective function, was utilized with real-world wind speed data from SR-25 station Brazil assess applicability modeling parameters. Notably, HBMFA achieved superior solution accuracy, enhancements averaging 0.025% compared conventional FA, despite moderate increase execution time approximately 18.74%. Furthermore, this dominance persisted when performance other common algorithms. However, some limitations exist, longer HBMFA, raising concerns about its practical scenarios where computational efficiency is critical. while demonstrates improvements values, establishing significance these differences FA not consistently achieved, which warrants further investigation. Nevertheless, added value work lies advancing state-of-the-art algorithms, particularly enhancing accuracy for critical engineering applications.

Язык: Английский

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A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Energy Reports, Год журнала: 2024, Номер 11, С. 6109 - 6125

Опубликована: Июнь 1, 2024

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked lot of interest over the last few years due to their robust efficiency, low operating temperature, longevity. The PEMFC's electrochemical model has seven unknown parameters, which not given in manufacturer's datasheets need be accurately estimated present more accurate model, leading improved efficiency performance PEMFC systems. estimation those parameters been dealt with as complex non-linear optimization problem that needs powerful algorithm solve it. existing algorithms still some disadvantages, such falling into local minima convergence speed, make them ineligible this complicated acceptable accuracy computational cost. Therefore, study presents new parameter technique for estimating accurately, thereby achieving precise modeling PEMFCs. This called IKOA is based on integrating Kepler (KOA) novel Lévy-Normal (LN) mechanism strengthen its exploration exploitation capabilities against multimodal problem. Lévy flight aims improve KOA's operator accelerate speed toward near-optimal solution, thus minimizing cost; meanwhile, normal distribution used operator, aiding escape minima. proposed KOA herein evaluated several rival using six well-known commercial stacks highlight effectiveness. Key metrics cost, fitness measures, statistical validation through Wilcoxon rank-sum test IKOA's effective enhancing predictive operational numerical findings show high superiority all optimizers solved benchmarks.

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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An adaptive chaotic league championship algorithm for solving global optimization and engineering design problems

Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200511 - 200511

Опубликована: Март 1, 2025

Язык: Английский

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 662 - 671

Опубликована: Янв. 1, 2025

Язык: Английский

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