Parameter Estimation of Three-Diode Photovoltaic Model Using Reinforced Learning-Based Parrot Optimizer with an Adaptive Secant Method DOI Open Access

Nandhini Kullampalayam Murugaiyan,

C. Kumar,

Magdalin Mary Devapitchai

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10603 - 10603

Published: Dec. 3, 2024

In the developing landscape of photovoltaic (PV) technology, accuracy in simulating PV cell behaviour is dominant for enhancing energy conversion efficiency. This study introduces a new approach parameter estimation three-diode model, basis representation characteristics. The methodology combines reinforced learning-based parrot optimizer (RLPO) with an adaptive secant method (ASM) to fine-tune parameters governing model. RLPO algorithm inspired by mimetic ability parrots, i.e., foraging, staying, communicating, and fear noticed trained Pyrrhura Molinae as it influences learning mechanisms adaptively explore exploit search space optimal sets. Simultaneously, ASM enhances convergence rate through iterative adjustment mechanism, responding curvature objective function, thereby ensuring estimation. combination addresses complexities non-linearities inherent offering robust framework Through extensive simulations, proposed demonstrated superior performance terms accuracy, speed, reliability when compared existing algorithms. empirical results emphasize effectiveness integrating strategy handling details model parameterization. These outcomes show that can handle issues related optimization systems, opening door progress sustainable technologies.

Language: Английский

Fitness-Guided Particle Swarm Optimization with Adaptive Newton-Raphson for Photovoltaic Model Parameter Estimation DOI
M. Premkumar,

R. Sowmya,

Tengku Juhana Tengku Hashim

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112295 - 112295

Published: Oct. 1, 2024

Language: Английский

Citations

5
PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network DOI Creative Commons
Wai‐Lun Lo, Henry Shu-Hung Chung, Tai-Chiu Hsung

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(10), P. 3006 - 3006

Published: May 9, 2024

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations research topics in technology. The parameters could be used for health monitoring fault diagnosis. Recently, a estimation method based neural network numerical current predictor methods has been developed. However, order to further improve accuracies, new approach is proposed this paper. output voltage dynamic responses measured, time series I-V vectors will as input an artificial (ANN)-based model range classifier (MPRC). MPRC trained using dataset with large variations parameters. results preset initial particles' population particle swarm optimization (PSO) algorithm. PSO algorithm estimate derivation maximum power point tracking (MMPT). Simulations on experimental generated by simulation show that algorithms can achieve up 3.5% accuracy speed convergence was significantly improved compared purely approach.

Language: Английский

Citations

4
Physics‐Based Machine Learning Electroluminescence Models for Fast yet Accurate Solar Cell Characterization DOI Creative Commons

Erell Laot,

Jean‐Baptiste Puel,

Jean‐François Guillemoles

et al.

Progress in Photovoltaics Research and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

ABSTRACT Electroluminescence analyses of solar cells and modules allow for fast, cost‐effective, nondestructive spatial characterization devices at different stages their development use. Voltage‐dependent electroluminescence (ELV) measurements have been shown to mimic diode voltage–current characteristics. A derived physical model enables the determination two local pseudoparameters from ELV data measured on silicon cells: a pseudorecombination current pseudoseries resistance . Local characteristics cells, such as series or dark saturation , can be deduced these pseudoparameters. are stored in large cubes, typically containing few hundred thousand pixels. Pixel‐wise regression is commonly achieved through nonlinear least squares (NLLS) minimization; knowing that luminescence image 6 ′ cell contains about 1 Mpix, this method time‐consuming, necessitating trade‐off between sample size, resolution, fitting accuracy, computation duration. We hence propose replace NLLS with machine learning (ML) techniques, known efficiency rapidly processing datasets. compare performances multilayer perceptron (MLP) ones convolutional neural network (CNN) called modified U‐NET (mU‐NET). The first ML conducts pixel‐wise analysis cube second processes entire single step. present comprehensive prediction objectively assessing advantages limitations proposed techniques. Our step ensure precision sufficient valid comparison deviation accuracy models compared almost negligible MLP 3.1 % when employing mU‐NET, demonstrating relevancy operational application. Both fast efficient: time required decreases by factor 240 1200 method.

Language: Английский

Citations

0
Performance estimator of photovoltaic modules by integrating deep learning network with physical model DOI
Shinong Wang, Zheng Wang, Yuan Ge

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136171 - 136171

Published: April 1, 2025

Language: Английский

Citations

0
Modeling and Simulation of Simplified Quadruple Diode Solar PV Module Under Influence of Environmental Conditions and Parasitic Resistance DOI Open Access

Ali M. Jasim,

Бараа М. Албакер, Hussein Jumma Jabir

et al.

TEM Journal, Journal Year: 2024, Volume and Issue: unknown, P. 757 - 770

Published: Feb. 27, 2024

To ensure a rapid and consistent design of Photovoltaic (PV) modules, the presence an effective simulator is essential for assessing behaviour PV cell when subjected to or partial changes in temperature, irradiance, parasitic resistance. The prevailing approach modelling involves utilizing equivalent circuit that encapsulates both nonlinear linear mechanisms. study introduces eleven-parameter quartic model simulation unit. takes into account constraints validated by solving non-linear voltage equation. examination concentrates on three pivotal junctures: open circuit, maximum power point, short circuit. These key points are crucial comprehending operational characteristics module typical conditions. proposed quadruple diode has been demonstrated outperform lower-order models terms performance accuracy. validate strength precision developed model, was simulated using specifications panels, outcomes were compared with recorded values obtained from models.The tested standard mathematical equations cells within MATLAB/ Simulink environment.

Language: Английский

Citations

1
Parameter Estimation of Three-Diode Photovoltaic Model Using Reinforced Learning-Based Parrot Optimizer with an Adaptive Secant Method DOI Open Access

Nandhini Kullampalayam Murugaiyan,

C. Kumar,

Magdalin Mary Devapitchai

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10603 - 10603

Published: Dec. 3, 2024

In the developing landscape of photovoltaic (PV) technology, accuracy in simulating PV cell behaviour is dominant for enhancing energy conversion efficiency. This study introduces a new approach parameter estimation three-diode model, basis representation characteristics. The methodology combines reinforced learning-based parrot optimizer (RLPO) with an adaptive secant method (ASM) to fine-tune parameters governing model. RLPO algorithm inspired by mimetic ability parrots, i.e., foraging, staying, communicating, and fear noticed trained Pyrrhura Molinae as it influences learning mechanisms adaptively explore exploit search space optimal sets. Simultaneously, ASM enhances convergence rate through iterative adjustment mechanism, responding curvature objective function, thereby ensuring estimation. combination addresses complexities non-linearities inherent offering robust framework Through extensive simulations, proposed demonstrated superior performance terms accuracy, speed, reliability when compared existing algorithms. empirical results emphasize effectiveness integrating strategy handling details model parameterization. These outcomes show that can handle issues related optimization systems, opening door progress sustainable technologies.

Language: Английский

Citations

1