摘要
针对水电站运行人员巡检时间过长,检查设备故障效率过低等问题,设计了水电站故障检测方案。根据改进的Adaboost方法对不同工况下机器作用所产生的噪声值进行训练,并建立一个分类器模型,将其应用到水电站设备故障检测方案当中。通过仿真实验,结果表明改进的Adaboost分类器正确率很高,达到89.1%。此方案可以提高水电站设备故障的检测效率,加强了工作人员的安全保障。
In view of longer operation personnel inspection time and lower equipment tault check efficiency in hydropower station, a fault detection scheme is designed, in which, the noises generated by the operation of machines in different operation conditions are trained according to improved Adaboost method and a classifier model is set up. The model is applied to equipment fault detection scheme of hydropower station. The simulation experiment results show that the improved Adaboost classifier has a high correct rate of 89.1% . The scheme can improve the detection efficiency of equipment fault of hydropower station and improve the security of staffs.
出处
《水力发电》
北大核心
2018年第3期62-65,共4页
Water Power
作者简介
陈涛（1982-），男，甘肃兰州人，高级工程师，博士，现从事电力环保与职业卫生工作．
