Photovoltaic panel anomaly detection system based on Unmanned Aerial Vehicle platform

Abstract

The emergence and rapid development of the Unmanned Aerial Vehicle (UAV) Photovoltaic inspection system have become an effective means of solving the operation and maintenance of photovoltaic power plants. In order to cooperate with the current UAV platform for photovoltaic panel anomaly detection, this paper proposes a photovoltaic infrared target anomaly detection system. In this paper, the Sobel operator is used to extract the photovoltaic slab area of the image, and the canny operator is used to obtain the photovoltaic small plate area to realize the complementary advantages. At the same time, the deep learning is applied to the algorithm to learn the discriminative characteristics of the image, and the brightness statistics are used. The method is supplemented by the method to achieve anomaly detection of the photovoltaic panel area. The effectiveness of the proposed algorithm is proved by a large number of experiments.

İHA Tarafından Elde Edilen Termal Görüntüler Kullanılarak Fotovoltaik Sistemde Derin Öğrenme Tabanlı Arıza Tespiti ve Teşhisi

“…In the training of the algorithm, solar panel errors are detected in the images by using CNN as a deep learning method. As a result of the studies, a success rate of 90.91% was achieved [14]. In the study by Venkatesh and Sugumaran, errors in solar panels are detected by CNN deep learning method.…”

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

“…Tis method enables us to separate the background and the modules using the binary thresholding much more easily, and it can be controlled using software [28]. Te next stage is implementing module edge detection with canny edge detection which Xie et al used [29]. Tis only produces little noise, unlike other methods, such as Sobel edge detection [38] and Laplacian edge detection [39], as shown in Figure 8.…”

“…Tis only produces little noise, unlike other methods, such as Sobel edge detection [38] and Laplacian edge detection [39], as shown in Figure 8. While line detection in each module is performed using the Hough line transform by Gao et al [23], it can only obtain the largest and longest continuous line on the image and cannot obtain more detailed lines [29]. Also, for a large-scale PV plant, it would not be efective for the visualization itself as the line would not be seen in higher altitude.…”

“…Te detection is carried out after images are captured by a thermal camera or UAV and then are processed in computer software. Te reviews, listed in Table 1, cover several insights regarding several algorithms used such as digital image processing (DIP) [23][24][25][26][27][28][29][30], deep learning (DL) [31], and other machine learning (ML) techniques [26,32], which have comparable results to detect modules with or without faults and classify it into a category. Despite that, those approaches were able to detect the module's fault but do not address its correlation to the module itself.…”

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

“…At the same time, multirotors are flexible in flight, with hovering and slow-flying functions, and are used in electric power inspection [6], border surveillance [7], modern agriculture [8], forest fire control [9], flood control, and disaster resistance [10], playing an increasingly important role in other fields too. However, the failures of multirotors such as communication faults, sensor failures, and power system anomalies are inevitable [11][12][13][14]. These failures may lead to mission interruption, multi-rotor crashes, and even casualties.…”