|
|
|
| Incipient Fault Detection of Generators in Wind Turbines Based on Sample Entropy |
| XIE Ping,WANG Yi-fan,JIANG Guo-qian,HUANG Meng-jun,HE Qun |
| Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract In order to test the generator incipient faults, such as stator windings inter-turn and broken rotor bars, which have such characteristics with small amplitude, non-stationary and susceptible to load variations, a sample entropy algorithm is introduced to perform fault feature extraction from the stator current and electromagnetic torque signals of generators in wind turbines. The proposed method is used to analyze fault signals under different load conditions to realize the quantitative representation of faulty signals. The results demonstrate that sample entropy algorithm is applicable to achieve fault characteristics quantification with data of short length, especially under varying operations and noise environments and it has great potentials in early fault detection and real-time online monitoring.
|
|
Received: 08 July 2015
Published: 11 August 2017
|
|
|
|
Corresponding Authors:
XIE Ping
E-mail: pingx@ysu.edu.cn
|
|
|
|
［1］Yang W, Tavner P J, Crabtree C J, et al. Wind turbine condition monitoring: technical and commercial challenges［J］. Wind Energy, 2014, 17(5): 673-693.
［2］张征平, 陈艳峰. 小波分析在高压电机故障检测中的应用［M］. 北京:中国电力出版社, 2009.
［3］Briz F, Degner M W, Diez A B, et al. Online diagnostics in inverter-fed induction machines using high-frequency signal injection［J］. IEEE Transactions on Industry Applications, 2004, 40(4): 1153-1161.
［4］Wolbank T M. On-line detection of stator winding faults in controlled induction machine drives［C］//SDEMPED 2005. 5th IEEE International Symposium on IEEE, Vienna, Austria. 2005: 1-6.
［5］Xu B Q, Chu Y L, Sun L L, et al. On-line methods to detect broken rotor bars in squirrel cage asynchronous motors［C］//ICEMS. International Conference on IEEE, Seoul, Korea. 2007: 1107-1111.
［6］牛发亮, 黄进, 杨家强, 等. 基于感应电机启动电磁转矩 Hilbert-Huang 变换的转子断条故障诊断［J］. 中国电机工程学报, 2005, 25(11): 107-112.
［7］谢平, 江国乾, 武鑫, 等. 基于多尺度熵和距离评估的滚动轴承故障诊断［J］. 计量学报, 2013, 34(6) : 548-553.
［8］席旭刚, 朱海港, 罗志增, 等. 基于经验模态分解样本熵的肌电信号识别［J］. 计量学报, 2014, 35(6): 534-539.
［9］符玲, 何正友, 麦瑞坤, 等. 近似熵算法在电力系统故障信号分析中的应用［J］. 中国电机工程学报, 2008, 28(28): 68-73.
［10］Yang W, Tavner P J, Court R. An online technique for condition monitoring the induction generators used in wind and marine turbines［J］. Mechanical Systems and Signal Processing, 2013, 38(1): 103-112.
［11］时献江. 异步电机及其驱动设备的无传感器故障诊断方法［D］.哈尔滨: 哈尔滨理工大学, 2009. |
|
|
|
2017, Vol. 38 
