Parameters Estimation of PV Models Using Artificial Neural Network

Abdellatif, Hussein; Hossain, Md. Ismail; Abido, M. A.

doi:10.1007/s13369-022-06901-7

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…A sufficient dataset was selected, and the model was effectively trained using the "nntool" toolbox in MATLAB, resulting in achieving the minimum possible error. A high-precision change in the duty cycle ∆D was obtained, which was subsequently converted to the signal specific to the boost converter (Abdellatif, Hossain, & Abido, 2022;Jyothy & Sindhu, 2018;Li, Zhang, Xu, & Yang, 2014). The network needs to be educated before it is used, as was previously described.…”

Section: P(w) V(v)mentioning

confidence: 99%

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Meddah,

Belarbi,

Negadi

et al. 2024

Preprint

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This article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems.

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Section: P(w) V(v)mentioning

confidence: 99%

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Meddah,

Belarbi,

Negadi

et al. 2024

Preprint

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“…In studies [ 35 , 36 ], the ML parameter identification models for SDM provided good performance. There are many heuristic search algorithms, including bioinspired, that were adapted to solve the parameter identification task of the different solar cell models [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…There are many heuristic search algorithms, including bioinspired, that were adapted to solve the parameter identification task of the different solar cell models [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Table 4 displays a brief comparison of the parameter identification models from studies [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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