Automated detection-classification of defects on photo-voltaic modules assisted by thermal drone inspection

Abstract

A new computational procedure is proposed for the automated detection-classification of defects on photovoltaic (PV) modules-panels. Thermal imaging or IR thermography is an important and powerful non-destructive technique for the investigation of structural or operational defects on PV modules and when it is combined with drones can provide a fully automated inspection, detection and defect classification procedure. The aforementioned image processing approach adopts pre- and post-processing tools and methodologies assisting the infrared (IR) thermography for the evaluation of a photovoltaic (PV) module performance. In particular, the passive approach of IR thermography was adopted, a portable thermal imager was used for the in-situ acquisition of images that show the distribution of infrared luminance of the PV panel surface. The acquired images are processed and analyzed for the detection and classification of defects and hot spots on the module’s surface that are potential candidates for faulty operation. The proposed computational methodology adopts gaussian filters for the IR images, thresholding operations, morphological transformations and Artificial Neural Networks. The use of IR thermography assisted by Unmanned Aerial Vehicles (UAVs) for the inspection of PV modules-panels proved to be a very reliable and efficient tool towards the automated detection-classification of defects.

A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera

“…Meanwhile, Gurras A and team have introduced a new computational process for automated defect detection and classification on PV modules utilizing thermal imaging or IR thermography with assistance from UAVs. Their approach has proven to be a reliable and efficient tool for automated defect detection and classification (Gurras A, et al, 2021). Moreover, M. Waqar Akram and associates have conducted research on automatic detection of photovoltaic module defects in infrared images using isolated deep learning and developmodel transfer deep learning techniques.…”