Abstract
In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.
Highlights
Renewable energy sources (RESs), such as wave power, wind power, and solar energy, have undergone impressively fast evolution
The thermographic inspection data taken from a thermographic camera are converted into Comma Separated Values (CSV) files of temperatures first
These outputs and selected features are inputted into the convolutional neural network (CNN) models and the XGBoost model for individual learning and identification
Summary
Renewable energy sources (RESs), such as wave power, wind power, and solar energy, have undergone impressively fast evolution. The traditional method involves maintenance personnel patrolling the whole plant to take IR images for the detection of malfunctioning PV modules. There are three major types of malfunctioning PV modules, i.e., hot spots, potentialinduced degradation (PID) and open circuits. In this paper, we design a hybrid scheme with three procedures that use an infrared camera on a UAV to take IR images and analyze them using machine/deep learning algorithms to detect malfunctioning PV modules or classify malfunction types. The contributions of this study are briefly summarized as follows. (I) We improve the “gamma correction” image processing algorithm that is used to enhance the contrast between normal and abnormal cells and build the CNNbased procedure to detect PV module defects. (II) To stress the contrast of malfunctioning locations for the temperature dataset, we design a threshold function to preprocess the temperatures. (III) To the best of our knowledge, our proposals are the first to adopt temperature data for training. (IV) Our model can differentiate PIDs, open circuits and hot spots well. (V) A hybrid detection scheme with even better performance is proposed
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