An Intelligent Maximum Power Point Tracker for Photovoltaic Systems Based on Neural Network

Mohamed Mostefai(1*), Abdellah Miloudi(2), Yahia Miloud(3)

(1) University of Saida, BP 138, En–Nasr, Saida 20000, Algeria
(2) University of Saida, BP 138, En–Nasr, Saida 20000, Algeria
(3) University of Saida, BP 138, En–Nasr, Saida 20000, Algeria
(*) Corresponding author

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To maximize a photovoltaic (PV) system's output power, continuously tracking the maximum power point (MPP) of the system is necessary. The MPP depends on irradiance conditions, the panel's temperature, and the load connected. In this paper, an intelligent system for attaining maximum power point tracking of PV systems is proposed. In this method, two outputs of neural network are used to provide the optimum voltage and monitor the state of health of the photovoltaic installation at the same time. If there's a difference between the calculated power and the maximum power point, the second output of the neural network is set to 1 and an alarm is triggered. The method is tested in a 1.2kw PV system under several conditions (normal operating and default). Finally, the results of simulation are included and explained to validate the proposed techniques.
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Photovoltaic; Neural Network; MPPT; Fault Detection

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