Prediction of Photovoltaic (PV) Thermal Fault Location Using the Catadioptric Device

Photovoltaic (PV) farms are subject to thermal faults that can degrade and reduce module efficiency. The currently used method of detecting thermal fault is time-consuming and hard to locate the fault position, especially in large PV areas. It can result in additional flaws, such as igniting the PV farm. We provide a new approach for monitoring and detecting PV thermal faults through a catadioptric device (CD) that offers fast and continual detection. As an early development stage, in this study, we concentrate on building a mathematical model that can determine the thermal fault coordinate based on two object images from different CD positions. The experiment then verifies the model, and the parameter variation is performed to find the coordinate prediction characteristic. Also, a case study simulation on large PV system monitoring was performed to figure out the thermal fault localization process. The result shows that the mathematical model can be used to determine the coordinate position of the thermal fault with acceptable measurement error. The parameter
$x_{k}$
tends to affect the average measurement error of the coordinate prediction and the error gradient of each axes couple. Greater the
$x_{k}$
, smaller measurement and gradient error can be achieved. In addition, the case study simulation result shows that the thermal fault position can be predicted with the worst percentage error (PE) of less than 10%, with a mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) in a reasonable value. Also, the sensitivity pattern can be used for CD condition monitoring.