Effect of Incidence Angle on Temperature Measurement of Solar Panel with Unmanned Aerial Vehicle-Based Thermal Infrared Camera

Abstract

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Keywords

• multiple regression analysis
• UAV
• solar panel
• TIR
• incident angle
• hotspot areas