TY - GEN
T1 - Large Scale Photovoltaic (PV) Farm Hotspot Detection Using Fish Eye Lens
AU - Pramana, Putu Agus Aditya
AU - Dalimi, Rinaldy
N1 - Funding Information:
The authors would like to express special thanks and acknowledgment to Indonesia Endowment Fund for Education (LPDP) which gave the financial support for this project.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/27
Y1 - 2020/9/27
N2 - The requirement to use low carbon-emitting power plants promotes increased utilization of renewable energy source. Photovoltaic (PV) as a modular generation is relatively easy to implement compared to the other renewable energy plants. In 2017, PV usage hits 100GW, globally. The safety of photovoltaic modules appears to be an issue with the growing usage of photovoltaic, as PV modules will face various modes of faults during the operation. Nearly 50 % of the total fault is the hot spot that is very difficult to locate in a largescale PV field. The latest method requires up to 105 days to detect hotspot in a 15 MW PV generation with an area of 30 hectares and made of 63000 modules (contains millions of cells). Those methods which cannot quickly and continuously detect the fault can degrade and burn the module. Therefore, a fast detection method is needed to prevent the catastrophic failure of PV modules. Thermal imaging using fish eye lens is promised to face this problem. It has wide field of view so that the wide area PV farm could be monitored simultaneously. However, fish eye lens has non linear projections which affect the image shape. Therefore, in this paper, the simulation to identified PV image characteristic that created by fish eye lens has been performed. The results show that there are some parameter combinations which can create a clear image without any overlapping. Also, the result show the length characteristic of PV image which can be used to defined the requirement of thermal sensor sensitivity.
AB - The requirement to use low carbon-emitting power plants promotes increased utilization of renewable energy source. Photovoltaic (PV) as a modular generation is relatively easy to implement compared to the other renewable energy plants. In 2017, PV usage hits 100GW, globally. The safety of photovoltaic modules appears to be an issue with the growing usage of photovoltaic, as PV modules will face various modes of faults during the operation. Nearly 50 % of the total fault is the hot spot that is very difficult to locate in a largescale PV field. The latest method requires up to 105 days to detect hotspot in a 15 MW PV generation with an area of 30 hectares and made of 63000 modules (contains millions of cells). Those methods which cannot quickly and continuously detect the fault can degrade and burn the module. Therefore, a fast detection method is needed to prevent the catastrophic failure of PV modules. Thermal imaging using fish eye lens is promised to face this problem. It has wide field of view so that the wide area PV farm could be monitored simultaneously. However, fish eye lens has non linear projections which affect the image shape. Therefore, in this paper, the simulation to identified PV image characteristic that created by fish eye lens has been performed. The results show that there are some parameter combinations which can create a clear image without any overlapping. Also, the result show the length characteristic of PV image which can be used to defined the requirement of thermal sensor sensitivity.
KW - fault
KW - fish eye
KW - hotspot
KW - PV module
UR - http://www.scopus.com/inward/record.url?scp=85097779426&partnerID=8YFLogxK
U2 - 10.1109/SCOReD50371.2020.9251016
DO - 10.1109/SCOReD50371.2020.9251016
M3 - Conference contribution
AN - SCOPUS:85097779426
T3 - 2020 IEEE Student Conference on Research and Development, SCOReD 2020
SP - 505
EP - 509
BT - 2020 IEEE Student Conference on Research and Development, SCOReD 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Student Conference on Research and Development, SCOReD 2020
Y2 - 27 September 2020 through 28 September 2020
ER -