Alla Eddine Toubal Maamar, Samir Ladjouzi, Rachid Taleb, Yacine Kacimi, "DETECTION AND CLASSIFICATION OF DEFECTS IN A PHOTOVOLTAIC SYSTEM USING THE NEURONAL APPROACH", J. Sc. & Tech, 2019 Vol 01, Issue 01 -6, 2019, pp 27 - 32, 2019
Nowadays, Photovoltaic has quickly become one of the most important renewable energy technologies... more Nowadays, Photovoltaic has quickly become one of the most important renewable energy technologies; this energy is widely used in a number of applications due to its advantages, including non-polluting energy. During its functioning, a photovoltaic system can be submitted to various defects, which require a certain approach to detect them for restoring the system to a normal state. There are several techniques and methods for the diagnostic of a photovoltaic system. In this work, we were specifically interested in the detection and the localization of the defects of the photovoltaic module using artificial neural networks, these networks are an important branch in the field of artificial intelligence, they are based on the establishment of learning algorithms that allow finding the best solution to some problems, a general overview of the neural network and their characteristics are discussed, then a multilayer perceptron MLP network is made to detect and classify some defects of a photovoltaic module.
Keywords— Photovoltaic System, Diagnostic of Systems, Detection and Classification of Defects, Artificial Neural Networks ANNs, Learning Algorithm
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Keywords— Photovoltaic System, Diagnostic of Systems, Detection and Classification of Defects, Artificial Neural Networks ANNs, Learning Algorithm
Keywords— Photovoltaic System, Diagnostic of Systems, Detection and Classification of Defects, Artificial Neural Networks ANNs, Learning Algorithm