scholarly journals MPPT Based On Artificial Neural Networks (ANN) For a Photovoltaic System Under Unstable Environmental Conditions

2021 ◽  
Author(s):  
Pascal Kuate Nkounhawa ◽  
Dieunedort Ndapeu ◽  
Bienvenu Kenmeugne
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Conditions ◽  
Experimental Tests ◽  
Photovoltaic System ◽  
Neural Controller ◽  
Point Tracking ◽  
Artificial Neural ◽  
Output Characteristics ◽  
Power Point

Abstract In this article, an artificial neural networks (ANN) based maximum power point tracking controller (MPPT) was developed to improve the performance of the FL-M-160W solar panel under unstable environmental conditions. To develop and configure the neural controller, a database resulting from experimental tests was built for the training of the proposed model. Then the model was tested and validated under the Matlab / Simulink environment. The optimum voltage obtained at the output of the neural controller is compared to the voltage of the photovoltaic generator and the error is used to modify the duty cycle of the DC-DC boost converter. It is shown after simulations that unlike conventional controllers which are very slow, the neural MPPT controller offers more stable, more accurate output characteristics with very low response time and very low oscillations around the operating point both in transient and steady state, even under varying environmental conditions.

scholarly journals Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1260
Author(s):  
César G. Villegas-Mier ◽  
Juvenal Rodriguez-Resendiz ◽  
José M. Álvarez-Alvarado ◽  
Hugo Rodriguez-Resendiz ◽  
Ana Marcela Herrera-Navarro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Electrical Energy ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Artificial Neural ◽  
Low Efficiency ◽  
Power Point

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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