Journal of Engineering Research, Год журнала: 2024, Номер unknown
Опубликована: Дек. 1, 2024
Язык: Английский
Electrical Engineering, Год журнала: 2024, Номер 106(5), С. 6565 - 6585
Опубликована: Апрель 20, 2024
Язык: Английский
Процитировано
15
Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 425, С. 116964 - 116964
Опубликована: Апрель 5, 2024
Язык: Английский
Процитировано
14
Expert Systems with Applications, Год журнала: 2024, Номер 253, С. 124333 - 124333
Опубликована: Май 27, 2024
Язык: Английский
Процитировано
8
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
Процитировано
8
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
Процитировано
7
Cogent Engineering, Год журнала: 2024, Номер 11(1)
Опубликована: Июль 23, 2024
In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary develop an appropriate PV cell model and accurately determine unknown parameters associated with model. The process extracting for models complex optimization issue that involves nonlinearity multiple models. Accurate estimation characteristics units crucial since these factors significantly affect systems in terms power current generation. Consequently, this research presents advanced methodology, known as Pelican Optimization Algorithm (POA), aimed find optimal values unspecified units. study, Single Diode Model (SDM) employed analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, well STM6-40/36 panels. POA algorithm utilized solar modules. Furthermore, enhance precision obtained solutions, study incorporates Newton–Raphson (NR) method into algorithm. achieves optimum Root Mean Square Error (RMSE) (RTC STP6-120/36) are found be 7.7298E-04, 2.0528E-03, 1.7220E-03 1.4458E-02 respectively. From results, observed that, exhibit superior compared other MH algorithms. statistical findings show has higher average robustness accuracy than
Язык: Английский
Процитировано
4
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
Процитировано
0
Mathematics, Год журнала: 2025, Номер 13(3), С. 405 - 405
Опубликована: Янв. 26, 2025
The Kepler optimization algorithm (KOA) is a metaheuristic based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets for various issues. However, the KOA hampered by limitations insufficient convergence accuracy, weak global search ability, slow speed. To address these deficiencies, this paper presents multi-strategy fusion (MKOA). Firstly, initializes population using Good Point Set, enhancing diversity. Secondly, Dynamic Opposition-Based Learning applied individuals to further improve its exploration effectiveness. Furthermore, we introduce Normal Cloud Model perturb best solution, improving rate accuracy. Finally, new position-update strategy introduced balance local search, helping escape optima. MKOA, uses CEC2017 CEC2019 suites testing. data indicate that MKOA more advantages than other algorithms terms practicality Aiming at engineering issue, study selected three classic cases. results reveal demonstrates strong applicability practice.
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 1, 2025
Abstract This paper presents a study to enhance the performance of recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well exploitation properties. A new set algorithms, namely Prairie dog optimization algorithm, INFO, Fission fusion (FuFiO) are included in fundamental framework NMRA operation. The proposed is hybrid based on four algorithms: Dog, Fusion Naked (PIFN) algorithm. Five mutation operators/inertia weights exploited make self-adaptive nature. Apart from that, stagnation phase added for avoidance. tested variable population, dimension size, efficient parameters analysed Friedman Wilcoxon rank-sum tests performed determine effectiveness PIFN On basis comparison outcomes, more effective robust than other techniques evaluated prior researchers address standard benchmark functions (classical benchmarks, CEC 2017, CEC-2019) complex engineering design challenges. Furthermore, reliability demonstrated testing using various PV modules, RTC France Solar Cell (SDM, DDM), Photowatt-PWP201, STM6- 40/36, STP6-120/36 module. results obtained compared with MH algorithms reported existing literature. achieved lowest root-mean-square error value, (SDM) 7.72E−04, (DDM) 7.59E−04, module 1.44E−02, STM6-40/36 1.723E−03, Photowatt-PWP201 2.06E−03, respectively. In order accuracy parameter estimation solar photovoltaic systems, we integrated Newton-Raphson approach Experimental statistical further prove significance respect algorithms.
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 2, 2025
Modelling the circuit model parameters of photovoltaic (PV) cells and modules is one significant encounters in field solar energy. Lately, with advance application optimization algorithms, approximating PV module can be changed into an problem. This research offers pipeline for optimal collection systems. The method founded on a novel combination metaheuristic algorithm, termed AHEO (Adapted Human Evolutionary Optimizer) current goal. key purpose employing paper to minimalize root mean square error (RMSE) between forecast measured I–V curves system. has been confirmed commercial results show its high accuracy RMSE decrease 34.6% related conventional methods.
Язык: Английский
Процитировано
0