Fault Diagnosis and Detection in Photovoltaic Systems Using Neural Network VGG16
Subject Areas : AI and Robotics
ُSamaneh Azimi
1
,
Mohammad Manthouri
2
*
,
Mehdi Akhbari
3
1 - Electrical and Electronic engineering department, Shahed university
2 -
3 - Electrical and Electronic engineering Department, Shahed university
Keywords: Photovoltaic array, Maximum power point tracking, Fault classification, Convolutional neural network, Scalograms,
Abstract :
Fault detection in photovoltaic (PV) arrays is necessary to increase the output power and also the useful life of a PV system. The presence of conditions such as partial shade, high impedance faults, and the maximum power point detector (MPPT) system make the fault detection of PV in environmental conditions more challenging. The literature identified and classified defects just in few scenarios. In this study two-dimensional scalograms are generated from PV system data. The VGG16 as a pretrained convolutional neural network is used for feature extraction. Finally, to identify and classify faults in the PV system a fully connected neural network is trained. Unlike the previous methods proposed in the literature on the subject of defect detection and classification, various defective cases with MPPT combination are considered in this research. It has been shown that the proposed method including pre-trained CNN performs better than the existing methods and achieves an error detection accuracy of 83.375%.
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