The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers

Abstract

The subject of this work is the analysis of methods of detecting soiling of photovoltaic panels. Environmental and weather conditions affect the efficiency of renewable energy sources. Accumulation of soil, dust, and dirt on the surface of the solar panels reduces the power generated by the panels. This paper presents several variants of the algorithm that uses various statistical classifiers to classify photovoltaic panels in terms of soiling. The base material was high-resolution photos and videos of solar panels and sets dedicated to solar farms. The classifiers were tested and analyzed in their effectiveness in detecting soiling. Based on the study results, a group of optimal classifiers was defined, and the classifier selected that gives the best results for a given problem. The results obtained in this study proved experimentally that the proposed solution provides a high rate of correct detections. The proposed innovative method is cheap and straightforward to implement, and allows use in most photovoltaic installations.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

“…The technique also used the gray level co-occurrence matrix (GLCM) method to extract texture features from the hue layer, and then used a linear classifier to classify the PV panels as clean or dirty. Czarnecki et al [65] proposed several different algorithms to classify PV panels as clean or dirty using the k-nearest neighbor classifier, naive Bayesian classifier, and Fisher linear discriminator, respectively. The authors explained the principle, steps, and parameters of each algorithm, and provided a flow chart for the algorithm.…”

“…Compared with on-site observation and regular cleaning, this method can scientifically calculate the effects of dust reduction on the amount of PV panel power generation by measuring the light transmittance, dust thickness, and pollution rate on the surface of the PV panel, thus providing a basis for cleaning cycles and plans. The method in [64] does not require any additional sensors or equipment and both the images of PV panels taken with existing cameras used in [64] and the images taken by drones used in [65] can be adapted to different lighting conditions and angles, which improves recognition accuracy. The methods in [63,64] can only detect and monitor dust and soil on the surface of PV panels and cannot handle other types of pollution, such as rain, snow, bird droppings, etc.…”

“…Determine the optimal cleaning solution based on image classification, and adjust the cleaning frequency according to the dust Unable to generate electricity on rainy days; takes up a lot of space [64] 2020 Does not require any sensors Can only detect overlays such as dust or soil [65] 2022 Use drones to capture images from different angles and heights Images captured by drones require preprocessing [66] 2020 GEE provides free satellite data and semi-automated processing GEE does not currently support the processing of high-resolution imagery (such as drone imagery)…”