Estimation of Electrical Efficiency of Photovoltaic Panels with Methods Based on Deep Learning Using Image

Document Type : Original Article

Authors

1 Faculty of Engineering, Bozorgmehr University of Qaenat, Qaen, Iran

2 2Department of Computer Engineering, Bozorgmehr University of Qaenat, Qaen, Iran

3 Department of Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran

4 Department of Physics, Qaenat Branch, Islamic Azad University, Qaenat, Iran

5 Department of Mechanical Engineering, Bozorgmehr University of Qaenat, Qaen, Iran

Abstract

Natural renewable energy sources are abundant and economically attractive, with zero or very low carbon emissions. On the other hand, harsh environmental and weather conditions, such as soil and dust accumulation, affect the efficiency of renewable energy sources and systems. Accordingly, the need for automated inspection of photovoltaic panels is becoming more critical as the demand for new solar energy system manufacturing and installation increases worldwide. This study introduces a new dataset of images of dusty and clean plates. Furthermore, a new convolutional neural network architecture is proposed to detect the voltage generated by the panel. In the following, the parameters taken from the environment and the voltage estimated by the proposed neural network are analyzed using the random forest regression algorithm, and the panel's efficiency is calculated. The proposed process deals explicitly with detecting dust accumulation in photovoltaic panels. The results obtained in this work have experimentally shown that the proposed system produces high detection rates. The proposed new method leads to the implementing of a more effective and efficient automatic cleaning technique for photovoltaic panels.

Keywords

TABRIZ JOURNAL OF ELECTRICAL ENGINEERING

Articles in Press, Corrected Proof
Available Online from 30 December 2023

Files

Share

How to cite

Statistics