Design of intelligent monitoring system for contact temperature of medium-voltage switch cabinets based on ZigBee technology and AI anomaly prediction
TL;DR
An intelligent monitoring system for the contact temperature of medium-voltage switch cabinets based on ZigBee technology and AI anomaly prediction, in which a lightweight AI temperature anomaly prediction model is embedded.
Abstract
In power systems, failures of medium-voltage switchgear contacts caused by high temperatures occur from time to time. Traditional temperature measurement methods are applied in enclosed and strong electromagnetic interference environments, which have certain limitations. This paper designs an intelligent monitoring system for the contact temperature of medium-voltage switch cabinets based on ZigBee technology and AI anomaly prediction. The system uses the CC2530 as its main control chip, adopts the DS18B20 digital temperature sensor to collect contact temperatures, and solves the problem of difficult power supply at the high-voltage end through a CT self-powered and battery switching solution. Based on the Z-Stack protocol stack, it realizes node networking and data transmission. Finally, LabVIEW is used to develop a host computer monitoring platform, in which a lightweight AI temperature anomaly prediction model is embedded. This model uses historical temperature data of switchgear contacts to train the Gradient Boosting Decision Tree (GBDT) algorithm, and can real-time predict potential sudden temperature rises within 5-10 minutes.