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Large photovoltaic (PV) generations are vulnerable to thermal faults where the location is difficult to determine. The pre-existing thermal fault detection method (mostly manual visual inspection) is time-consuming and non-continuously monitoring. It may lead to more defects, such as degrading the faulted PV cell or putting the PV farm on fire. We propose a novel PV thermal fault monitoring and detection method using a catadioptric device (CD), which promises quick and continuous detection. In this paper, as an early stage in CD development, we focus on constructing a model involving some parameters to identify the object image formation. Then, through the model, a simulation case study of the PV farm monitoring proses was performed where the CD is ostensibly placed in front of the PV arrays. We variated the model parameters and selected the combination that resulted in a symmetrical and non-overlapped PV array image, which was then analyzed to see the sensor requirements of the CD. The results showed that the developed model could simulate PV image formation on CD with good validity. Also, the case result found that nine parameter combinations produce symmetrical and non-overlapped PV images where the image ratio is more affected by the camera position than the focal length. Finally, the minimum sensor requirement is determined by the center length of the farthest PV image to monitor all of the PV arrays.

Photovoltaic (PV) Thermal Fault Monitoring Using the Catadioptric Device