Abstract: Aiming at the problems of single state detection mode and poor detection effect of existing substation equipment, a state detection method of Smart substation equipment based on intelligent perception and deep learning is proposed. Firstly, low-power thermal imagers are installed in four corners of the substation to monitor the status of the equipment in real time. Then, the thermal image of the equipment is processed by median filter and erosion technology. After getting the gray image, the image features are extracted by accelerated robust feature method, and the status of the equipment is preliminarily monitored. Finally, the image features are further analyzed based on the deep learning model to detect the faulty equipment. The proposed method is based on Tensorflow platform to demonstrate its performance. The results show that compared with other methods, the proposed method has higher detection accuracy and recall rate, and faster detection rate, which can intuitively and accurately grasp the equipment status of the substation.