Research on real-time monitoring and fault warning of power business hall equipment driven by Internet of Things

Aiming at the problems of equipment fault detection lag and high false alarm rate in the traditional static threshold early warning of electric power business hall, a real-time monitoring and fault early warning system based on Internet of things (iot) driving was studied. Based on the four-layer architecture of “perception, transmission, processing and application”, a ZigBee and LoRaWAN integrated heterogeneous communication network and edge-cloud collaborative data processing platform were constructed. High-frequency acquisition and low-delay transmission of multi-source sensor data from key devices such as self-service terminals and uninterruptible power supplies are realized. On this basis, a random forest-LSTM fusion early warning model is proposed, which combines 18-dimensional feature extraction and dynamic threshold adjustment mechanism to effectively improve the ability of early fault recognition. The experimental results show that in the operation process of 87 pilot business halls for 6 months, the system collects 4.3 TB of data, the early warning accuracy reaches 89.3%, the false alarm rate is reduced from 31.5% to 12.8%, the average fault discovery delay is shortened from 4.7 to 1.2 h, the unplanned downtime is reduced by 40%, and the operation and maintenance cost is reduced by 25%. The study verifies the feasibility and superiority of the Internet of things and intelligent analysis technology in the front-end equipment management of power service, which provides a scalable technology path for the intelligent operation and maintenance of power business halls.