Design and test for permanent magnet wind power generators based on converter controlling modeling

Author(s):  
Gao Jian ◽  
Huang Shoudao ◽  
Zhang Wenjuan
2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chin-Tsung Hsieh ◽  
Her-Terng Yau ◽  
Jen Shiu

This study proposes a method based on the cerebellar model arithmetic controller (CMAC) for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP). The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.


2019 ◽  
Vol 29 (2) ◽  
pp. 1-5 ◽  
Author(s):  
Kaihe Zhang ◽  
Xiaoyan Huang ◽  
Lijian Wu ◽  
Youtong Fang ◽  
Wenping Cao

2019 ◽  
Vol 55 (4) ◽  
pp. 3607-3616 ◽  
Author(s):  
Lingyun Shao ◽  
Wei Hua ◽  
Feng Li ◽  
Juliette Soulard ◽  
Z. Q. Zhu ◽  
...  

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