TY  - JOUR
T1  - Regional Guidance System for Cleaning Robots as a Result of Pollution of Solar Panels
AU  - Cantez, Emin
AU  - Sahın, Hasan
AU  - Efe, Omer Faruk
PY  - 2023
DA  - September
DO  - 10.55549/epstem.1350961
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 274
EP  - 279
VL  - 22
LA  - en
AB  - Solar energy production is provided by thermal and photovoltaic (PV) systems. Among them, PVs are considered one of the most important power generation systems that produce safe and sustainable energy. Depending on the outdoor conditions, PV systems get dirty over time due to dust, rain and environmental factors. Above the PV system When polluted, it causes a significant decrease in their efficiency. Especially in large photovoltaic power plants, it is seen that the panels are not polluted equally. But the cleaning robots system When it starts to clean, it cleans all the panels. Cleaning all clean and dirty panels causes energy consumption, material life and spare parts waste. In the system we have planned, it is planned to clean the panels by detecting the dirty panels with the camera and directing the cleaning robot only to the contaminated areas. Since only the contaminated areas will be cleaned, the energy production efficiency of the panels will increase and the cleaning time will be shortened.
KW  - Photovoltaic
KW  - Energy
KW  - Image processing
KW  - Object detection
KW  - Camera
CR  - Acikgoz H. (2022).A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl Energy,305. 117912..
CR  - Ali, M.U., Khan, H.F., Masud M., Kallu K.D., & Zafar, A. (2020) A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography. Solar Energy, 208,643–651.
CR  - Amidi, A., Amidi, S., Vlachakis, D., Paragios, N., & Zacharaki, EI. (2016) A machine learning methodology for enzyme functional classification combining structural and protein sequence descriptors. Lecture Notes in Computer Science, 9656, 728–738.
UR  - https://doi.org/10.55549/epstem.1350961
L1  - https://dergipark.org.tr/en/download/article-file/3366730
ER  -