Large Scale Photovoltaic (PV) Farm Hotspot Detection Using Fish Eye Lens

Abstract

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.