Frequency regulation of PV-reheat thermal power system via a novel hybrid educational competition optimizer with pattern search and cascaded PDN-PI controller

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102958 - 102958

Опубликована: Сен. 1, 2024

Язык: Английский

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A reinforcement learning-based ranking teaching-learning-based optimization algorithm for parameters estimation of photovoltaic models

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101844 - 101844

Опубликована: Янв. 9, 2025

Язык: Английский

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Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 16, 2025

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling critical due to many factors, such as inherent nonlinearity, existing complexity, wide range parameters. Although different researchers have recently proposed several effective techniques system parameter identification, it still an interesting challenge enhance accuracy modelling. With above motivation, this article suggests stage-specific mutation strategy enhanced differential evolution (EDE) that adopts better search process arrive at optimal solutions by adaptively varying factor crossover rate stages. The identification systems formulated single objective function. It appears in form Root Mean Square Error (RMSE) between current from experimental data calculated using identified considering constraints (limits). I-V (current-voltage) characteristics/data with are validated justify approach's efficacy cells modules. Extensive simulation has been demonstrated two (RTC France & PVM-752-GaAs) three modules (ND-R250A5, STM6 40/36 STP6 120/36). results obtained EDE technique show Errors 7.730062e-4, 7.419648e-4, 7.33228e-4 respectively, RTC models based on single, double, triple diodes. Also, RMSE involved PVM-752-GaAs diodes 1.59256e-4, 1.408989e-4, 1.30181e-4, respectively. ND-R250A5, 120/36 involve values 7.697716e-3, 1.772095e-3, 1.224258e-2, All these least compared other well-accepted algorithms, thereby justifying its higher accuracy.

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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A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

Chaos Solitons & Fractals, Год журнала: 2024, Номер 185, С. 115111 - 115111

Опубликована: Июнь 15, 2024

Язык: Английский

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Advanced control parameter optimization in DC motors and liquid level systems

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 9, 2025

Язык: Английский

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Accurate parameters extraction of photovoltaic models using Lambert W-function collaborated with AI-based Puma optimization method

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104268 - 104268

Опубликована: Фев. 1, 2025

Язык: Английский

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Improved Tasmanian devil optimization method for accurate parameter extraction of photovoltaic models in various temperature and irradiation conditions

International Journal of Modelling and Simulation, Год журнала: 2025, Номер unknown, С. 1 - 28

Опубликована: Фев. 26, 2025

High-performance solar photovoltaic models rely on a precise understanding of (PV) cell parameters. This necessity arises from the importance comprehending and optimizing performance systems to ensure reliable efficient energy production. Nonetheless, due intrinsic nonlinear nature systems, employing algorithm is essential for accurate modeling. In this article, an enhanced inspired by behavior Tasmanian Devil, named Improved Devil Optimization (ITDO), proposed enhance original TDO. Our includes improvements exploitation phase, increasing frequency prey detection attacks in target zone. The method retains steps, with exploration stage unchanged. However, second step has been adaptation mechanism, final improved efficiently select global optimum. These modifications do not impact method's complexity. To assess ITDO's effectiveness, experiments were conducted using single, double, PV module models. Thorough comparison seven other algorithms revealed superior solution accuracy. Additionally, statistical analyses Wilcoxon rank-sum Friedman tests confirmed superiority as most robust parameter estimation systems.

Язык: Английский

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Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models

PLoS ONE, Год журнала: 2024, Номер 19(7), С. e0308110 - e0308110

Опубликована: Июль 29, 2024

The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar as clean, abundant, cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation introducing the logarithmic spiral search selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA an improved version (AOA). It combines behavior mechanism to improve exploration capabilities. makes it easier obtain extraction. RTC France cell employed benchmark case in order ensure consistency impartiality. A standardized experimental framework integrates into tuning process for three models: single-diode, double-diode, three-diode choice underscores significance field, providing robust evaluation platform Ls-AOA. Statistical convergence analyses enable rigorous assessment. consistently attains low RMSE values, indicating current-voltage characteristic estimation. Smooth reinforces efficacy. Comparing other methods strengthens superiority optimizing model parameters, showing that potential use energy.

Язык: Английский

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Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

Processes, Год журнала: 2024, Номер 12(12), С. 2718 - 2718

Опубликована: Дек. 2, 2024

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits challenging due limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) determining ideal parameters circuit model. presented EBOA incorporates several modifications aimed at improving searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support BOA settings be adjusted an adaptive manner. Additionally, it integrates key mechanisms Hippopotamus (HO) strengthen algorithm’s exploitation potential by leveraging surrounding knowledge more effective position updates while also balance between global and local search processes. was subjected extensive mathematical validation through application benchmark functions rigorously assess performance. Also, PV parameter estimation achieved combining with Newton–Raphson approach. Numerous module cell varieties, including RTC France, STP6-120/36, Photowatt-PWP201, were assessed using double-diode single-diode models. higher performance shown statistical comparison many well-known metaheuristic techniques. To illustrate this, root mean-squared error values our scheme (SDM, DDM) PWP201 are follows: (8.183847 × 10−4, 7.478488 10−4), (1.430320 10−2, 1.427010 10−2), (2.220075 10−3, 2.061273 10−3), respectively. experimental results show that works better than alternative techniques terms accuracy, consistency, convergence.

Язык: Английский

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