Power equipment is one of the most important transmission and transformation equipment in power system. Improving the reliability of power equipment is of great significance to the safe and reliable operation of the whole power grid. A reliability tracking method of power system equipment based on Embedded Internet of things technology is proposed. The minimum cut set algorithm of reliability index based on Embedded Internet of things technology is given. In this paper, a fault tree analysis method for power system equipment unreliability index is proposed. According to the analysis of equipment structure, component function and failure mode, the fault tree model of power system equipment is established, and the reliability tracking calculation example is analyzed. Experiments show that the method can track the order stably, determine the key components that affect the reliability of the equipment, and identify the weak links of the equipment.

As the infrastructure for people’s production and life, the stable operation of power facilities is very important. As a key equipment in the operation of power facilities, transformers have become important power equipment for the daily maintenance of the power sector. In the past, electric power operation and maintenance personnel mostly used on-site visual inspection to preliminarily judge whether the transformer is operating normally. The disadvantage of this method is inaccuracy. A transformer condition monitoring technology based on a surface acoustic wave passive wireless intelligent sensing system is proposed to overcome the above shortcomings. Its working mechanism is to monitor the oil level, oil temperature and external ambient temperature of the cooling oil in the transformer in real time. Then, the operating status can be determined. The operating data is transmitted to the control center through the 4G network to help the operation and maintenance personnel to centrally monitor the status of the transformer, and then provide a pre-alarm function for abnormal conditions of the transformer.