摘要
为提升高速列车牵引系统的稳定性和可靠性,针对其牵引电机提出一种基于未知输入观测器的转子断条和速度传感器故障联合诊断方法.首先,通过非奇异坐标变换,将牵引电机系统解耦为两个分别只包含转子断条故障和速度传感器故障的子系统,实现转子断条故障与速度传感器故障的解耦,并进一步利用一阶低通滤波器将含速度传感器故障的子系统转化为增广系统.其次,对含转子断条故障的子系统和速度传感器故障增广系统分别设计未知输入区间观测器和未知输入滑模观测器.在此基础上,采用未知输入区间观测器上界和下界构建转子断条故障诊断的检测变量和自适应阈值,利用未知输入滑模观测器的等效输出控制原理实现速度传感器故障估计.最后,通过仿真和TDCS-FIB平台实验验证了所提方法的有效性和鲁棒性.
In order to improve the stability and reliability of the traction system of high-speed train,this paper proposes a simultaneous diagnosis method for broken rotor bar fault and speed sensor fault of traction motor based on the unknown input observer.Firstly,through non singular coordinate transformation,the traction motor system is decoupled into two subsystems that only contain broken rotor bar fault and speed sensor fault,respectively,so as to realize the decoupling of broken rotor bar fault and speed sensor fault,and the subsystem containing speed sensor fault is further transformed into augmented system by using first-order low-pass filter.Then,the unknown input interval observer and the unknown input sliding mode observer are designed for the subsystem with broken rotor bar fault and the speed sensor fault augmentation system,respectively.On this basis,the upper and lower bounds of the unknown input interval observer are used to construct the detection variables and adaptive thresholds for broken rotor bar fault diagnosis,and the speed sensor fault estimation is realized by using the equivalent output control principle of the unknown input sliding mode observer.Finally,the effectiveness and robustness of the proposed method are verified by simulation and TDCS-FIB platform experiments.
作者
许水清
柴晖
胡友强
黄大荣
张可
柴毅
XU Shui-Qing;CHAI Hui;HU You-Qiang;HUANG Da-Rong;ZHANG Ke;CHAI Yi(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009;School of Automation,Chongqing University,Chongqing 400044;School of Artificial Intelligence,Anhui University,Hefei 230601)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2023年第6期1214-1227,共14页
Acta Automatica Sinica
基金
国家自然科学基金(62273128,61803140,U2034209)
中国博士后面上基金(2020M682474)
重庆市技术创新与应用发展专项重点项目(cstc2019jscx-msxm X0073)
四川省川渝合作重点研发项目(2020YFQ0057)资助。
