Power equipment fault information acquisition system based on Internet of things

With the advent of the Internet of things era, power equipment is gradually connected to the network, and its intelligent fault detection function provides greater help for the power industry. The purpose of this study is to design the power equipment fault information acquisition system of the Internet of things. This research is based on the equipment fault information collection system of the Internet of things and mainly studies the fault information collection method based on the Internet of things technology. Equipment fault data are generally time series data. In the analysis of equipment failure, the data before and after fault and before and after fault are analyzed. The abnormal state of equipment is associated with the data before and after the fault. Therefore, by analyzing the characteristics of the fault data and the equipment before and after the fault, a bidirectional recurrent neural network model based on LSTM is constructed. The method designed in this paper can not only improve the efficiency and speed of collection, but also can compare and collect fault information. The overall operation state of the power unit is improved accurately. The research results show that the company's low-voltage user acquisition success rate has reached more than 99%. With the increase of time, the fault information collection efficiency can approach 99%. It shows that the function of this research system is better, the economic loss of the company is reduced, and the management is optimized.