Aiming at the problem that the identification and classification of infrared hot spots in photovoltaic modules require a large number of training samples and the accuracy needs to be improved, a hot spot recognition method based on the combination of persistent homology algorithm and convolutional neural network is proposed. The persistent homology algorithm in topological data analysis is first used to map the values on the RGB three channels in the infrared thermal image to the three-dimensional coordinate system to form a three-dimensional point cloud, and then the persistent homology calculation is carried out to extract the topological features contained in the picture in advance, and then the extracted features are vectorized and arranged in a fixed order, mapped to the pixels of the image, and combined with the brightness and contrast characteristics of the picture. Finally, the processed image data is input into the adjusted LeNet-5 convolutional neural network model to realize the classification and identification of photovoltaic infrared hot spots, and various performance indicators are calculated through the confusion matrix to evaluate the performance of the model. Experimental results show that the model effectively extracts the high-dimensional topological features hidden in the image, and complements and combines them with other features favorably, which solves the problem that the image data cannot be directly input into the persistent homology algorithm and the high-dimensional topological features cannot be directly used as input to the deep learning model. Additionally, the classification and recognition accuracy of photovoltaic infrared hot spots is improved, and the computing resources required are significantly reduced.
Key words
PV modules /
feature extraction /
convolutional neural network /
topological data analysis /
persistent homology /
hot spot
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