A power equipment monitoring system based on SE-Attention image recognition model

Abstract

The current power equipment monitoring is mainly based on the traditional classical neural network automatic monitoring mode, which has such problems as low accuracy and difficulty to dig out the deep information of the image, a power equipment monitoring system based on SE-Attention (squeeze excitation attention) image recognition model was thus designed. Based on the convolutional neural network (CNN), combined with the SE-Net (squeeze extraction network) to extract the local features of the image, the row attention mechanism, column attention mechanism and channel attention mechanism in deep learning were adopted to increase the weight of local fault information, mine the deep information, and improve the accuracy of power equipment fault identification. The experimental results show that compared with CNN and SE-Net detection methods, this detection method has different improvement in the recognition accuracy of arrester, circuit breaker, current transformer and voltage transformer.

Keywords

• se-attention mechanism
• power equipment monitoring
• attention mechanism
• deep information
• fault identification