Substation Equipment Fault Identification Based on Infrared Image Analysis

Abstract

As a key part of the power system, the substations undertake important work. With the development and construction of smart grids, the status data, image monitoring data, and environmental meteorological data of power systems are gradually being integrated and shared on a unified platform. Traditional models based on theoretical analysis are difficult to deal with multi-dimensional, massive data set information. Under this background, starting from the inherent law of the data itself, the use of machine learning methods combined with the infrared image data of the equipment can achieve intelligent identification and early warning of substation equipment failures. This paper first uses the convolutional neural network to identify the substation equipment in the picture, and then combines the infrared image to perform image registration. Finally, the deep belief network is used to determine whether the device is in wrong condition. The overall substation equipment fault identification is tested on real data, and the experimental results show that the proposed method has high accuracy.