Shanxi Xinjing Mining Coal Industry Co., Ltd.

针对阳泉市新景矿煤业有限公司使用的矿井变压器传统监测手段存在效率较低、智能化不足、劳动强度大、现场监控信号失真等问题,在分析了矿井变压器常见故障和异常特征的基础上,基于BP神经网络理论设计一种矿井变压器在线监测系统,完成硬件系统选型和软件控制系统设计,实现对变压器局部放电信号在线集中监测,准确识别声音异常、温度异常、过载故障及漏油故障。经在阳泉市新景矿井下2#变压器进行现场安装和调试后表明,提出的矿井变压器在线监测系统可准确识别设备异常,由以太网将现场信号发送到上位机监控系统,实现了对矿井变压器的集中监测和远程故障诊断,响应时间仅为1.83s,故障识别精度高,故障定位准确,取得了满意的应用效果,为后期实现矿井变压器的无人值守和远程运维提供应用参考。

In view of the problems of low efficiency, insufficient intelligenization, high labor intensity, distorted on-site monitoring signals and so on in the traditional monitoring mean for mine transformer used by Yangquan Xinjing Mining Coal Industry Co., Ltd., based on the analysis of common faults and abnormal characteristics of mine transformers, a mine transformer online monitoring system is designed based on BP neural network theory. The hardware system type selection and software control system design are completed to achieve online centralized monitoring of partial discharge signals of transformers, accurately identifying sound abnormalities, temperature abnormalities, overload faults, and oil leakage faults. After on-site installation and debugging of the 2transformer in the downhole of Xinjing Mine, Yangquan City, it shows that the proposed online monitoring system for mine transformers can accurately identify equipment abnormalities. The on-site signals are sent to the upper computer monitoring system through Ethernet, achieving centralized monitoring and remote fault diagnosis of mine transformers. The response time is only 1.83 seconds, with high fault identification accuracy and accurate fault positioning, achieving satisfactory application effects and providing application reference for the later realization of unattended operation and remote operation and maintenance of mine transformers.