Intelligent Inspection System of Power Equipment Based on Photoelectric Sensor/AR Technology

Abstract

The photoelectric wireless sensor network is composed of multiple photoelectric sensor nodes in the area. In addition to the basic sensing functions, the multiple micro and small photoelectric sensor stages contained in the area can also self-organize to form a wireless sensor network. According to the measurement method of power equipment and photoelectric sensor technology, the study equations the intelligent photoelectric wireless sensor structure of power equipment and the corresponding hardware composition. Meantime, the augmented reality (AR) technology is introduced to inspect the power equipment. Among them, multiple photoelectric sensors are concentrated on the power poles of the long-distance transmission line of the power grid and within 100 m around them, and meanwhile, a wireless sensor network centered on a single power pole is built in this area; the combination of AR and deep neural network (DNN) is used for the fault identification of power equipment. In the experiment, power equipment monitoring interface is generated based on the .NET framework, and data can be obtained with the help of the query button to realize the parameter monitoring of the power equipment on the client-server side. By binding the data source, the figure of power monitoring can be read and written in the database without modifying the display settings of the interface. The power measurement value is helpful for the dispatch of operators. With the help of ZedGraph, power data collected by the photoelectric sensor can be displayed on the interface corresponding to the dynamic data. Comparing the photoelectric sensor network of power poles and towers and the photoelectric sensor network of power poles that have not been constructed, it is confirmed that the power poles and towers sensor network can reduce the energy consumption and failure of detection data. Compared with SVM algorithm and BP neural network, DNN algorithm based on AR technology can conduct inspections accurately on failures of power equipment.