کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / نهمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

Fault diagnosis of photovoltaic modules using deep neural networks-VGG16

نویسندگان :

Samaneh Azimi¹ Mohammad Manthouri²

1- دانشگاه شاهد تهران 2- دانشگاه شاهد تهران

کلمات کلیدی :

Convolutional neural network Photovoltaic array،Fault classification،Maximum power point tracking،Scalograms

چکیده :

Fault detection in photovoltaic (PV) arrays is essential to increase the output power and lifetime of a PV system. Conditions such as partial shading, high impedance faults and the presence of Maximum Power Point Tracking (MPPT) make fault detection difficult under environmental conditions. Most research in this area has only identified and classified faults in a few scenarios. This study identifies and classifies faults in the PV system using two-dimensional Deep Convolutional Neural Networks (2-D CNN) and features extracted from two-dimensional scalograms generated from PV system data. Unlike previous methods proposed in the literature for fault diagnosis and classification, our study considered different fault cases with the combination of MPPT. This study shows that the proposed method, including a pre-trained CNN, performs better than existing methods and achieves 98.76 fault detection accuracy

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