Fault point detection of IOT using multi-spectral image fusion based on deep learning

Internet of Things (IoT) is widely applied in modern power systems, which could establish the intelligent power grid systems and obtain considerable social and economic benefits. IoT plays an important role in power grid safety production, user interaction, and information collection. However, existing methods cannot address problems of IoT devices accurately and quickly, such as fault detection. Aiming at the shortcomings of current power IoT equipment fault detection methods, this paper proposes a multi-spectral image fusion based on deep learning to detect fault points of power IoT equipment. The deep convolutional neural network is trained by simulating the image of the power device. The results show that the multi-spectral image descriptor based on deep learning presented in this paper shows very high accuracy in block matching, and the effect of image fusion is remarkable. This indicates that the proposed method can accurately integrate multi-spectral images of power equipment, helping to locate fault points quickly and accurately. (C) 2019 Published by Elsevier Inc.