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作 者:王永潭 郭勇[2] 王树新 王润鹏 王振羽 刘忠仁 王冠峰[4,5] WANG Yongtan;GUO Yong;WANG Shuxin;WANG Runpeng;WANG Zhenyu;LIU Zhongren;WANG Guanfeng(State Grid Xinyuan Company Ltd.,Beijing 100761,China;School of Computer Science,Haerbin Institute of Technology,Harbin 150001,China;Fengman Dam Reconstruction Engineering Bureau,Jilin 132000,China;Harbin Institute of Large Electric Machinery,Harbin 150040,China;Harbin Electric Machinery Company Ltd.,Harbin 150040,China)
机构地区:[1]国网新源控股有限公司,北京100761 [2]哈尔滨工业大学计算机学院,哈尔滨150001 [3]丰满大坝重建工程建设局,吉林吉林132000 [4]哈尔滨大电机研究所,哈尔滨150040 [5]哈尔滨电机厂有限责任公司,哈尔滨150040
出 处:《大电机技术》2019年第5期69-73,共5页Large Electric Machine and Hydraulic Turbine
基 金:2017年智能制造新模式应用项目(水力发电设备智能远程运维新模式)
摘 要:发电厂的电气设备具有较高的复杂性,不同的设备差异较大,出现故障时呈现出不同的特点,如何根据故障征兆快速准确找到故障原因变得更为困难。针对上述问题,设计了一套具有自学习功能的人机闭环的故障诊断方法。该方法将先验知识与设备故障率相结合,在进行设备故障诊断时引入了“改善因子”的概念。通过人在回路的方式获取设备的维修信息来计算“改善因子”,在进行故障原因判断时,将先验知识、设备运行数据及“改善因子”相结合来确定故障原因,以提高故障诊断准确性。最后,通过实例验证本方法的有效性。该方法已经应用于某些发电厂发电设备的故障诊断中,实践证明该方法能够更为高效、准确的定位故障原因。The electrical equipment of power plant has high complexity.There are great differences among different equipments.When equipment fails,it presents different characteristics.How to quickly and accurately find the cause of failure according to the symptoms of failure becomes more difficult.In view of the above problems,a set of fault diagnosis methods with self-learning function of human machine closed-loop is designed.This method combines prior knowledge with equipment failure rate,and introduces the concept of"improvement factor"when the device fault diagnosis is carried out.The"improvement factor"is calculated by obtaining maintenance information of equipment in man-in-the-loop way.In order to improve the accuracy of fault diagnosis,a combination of prior knowledge,equipment operation data and"improvement factor"is used to determine the cause of fault.Finally,an example is given to verify the effectiveness of this method.This method has been applied to the fault diagnosis of some power plant equipment.The practice proves that it can locate the cause of fault more efficiently and accurately.
分 类 号:TM622[电气工程—电力系统及自动化]
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