机器学习算法在大型发电机定子槽楔松紧度检测中的应用研究  

Research on Application of Machine Learning Algorithms into Detecting the Slot Wedge Tightness in Large-size Generator Stators

作　　者：马文博 MA Wenbo(Nuclear Power Operations Research Institute,200000)

机构地区：[1]核电运行研究(上海)有限公司,200000

出　　处：《电机技术》2023年第5期27-30,共4页Electrical Machinery Technology

摘　　要：各电厂在大修期间均需进行汽轮发电机定子槽楔松紧度检测工作,文章提出了一种基于机器学习算法的发电机槽楔松紧度故障检测方法。通过试验分析,选取确定了检测的有效特征参数。此方案可以应用到发电机槽楔松紧度的故障检测中。Power plants need to carry out the slot wedge tightness detection in the generator stators during the overhaul,a kind of generators'slot wedge tightness detection method based upon the machine learning algorithm is recommended in the paper.The effective feature parameters for the detection are selected and determined through the experimental analysis.This program is able to apply into the fault detection of the generator slot wedge tightness.

关 键 词：发电机 槽楔 松紧度检测 机器学习 

分 类 号：TM305[电气工程—电机]