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.
Highlights
Over the passing decades, the international photovoltaic (PV) market has experienced an undeniably impressive and rising growth in terms of installation and investments in the field, presenting an exponential rate in sales and in general use for various applications
With the application of the aforementioned numerical methodology to the data set of thermal images we can apply the computational procedure to test its capabilities on Unmanned Aerial Vehicles (UAVs) flight missions
When the digital image processing algorithm detects a hotspot on a particular thermal image from PV modules categorizes the fault into a severity stage, assess the fault detected and sends a detailed report back to the system controller for further actions such as scheduling of a new UAV flight
Summary
The international photovoltaic (PV) market has experienced an undeniably impressive and rising growth in terms of installation and investments in the field, presenting an exponential rate in sales and in general use for various applications. While there are several works [4,5] based on automated PV plant inspection systems, in their majority depend on classic image processing techniques, such as intensity thresholding. These techniques effective, they rely heavily on the manual tuning by the user and need calibration in every iteration for each individual image. With the use of both UAV flight scheduling and image processing computational procedure the aforementioned approach can be applied to large scale PV plants for fast and low cost inspections without sacrificing the accuracy of the results
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