Research on Fault Diagnosis for Pitch Sensors of Wind Turbines

Authors

Yanjie Zhai, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
Dinghui Wu, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
Yiyang Li, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Considering the biased output fault of pitch sensors for wind turbines, a fault diagnosis method based on the multi-innovation kalman filter algorithm was proposed. The corresponding relationship between the change of the pitch angle and the tiny displacement produced by the force acting on the tower was established according to the mechanical structure characteristics of wind turbines. More accurate estimated value for the tiny displacement was achieved by using the multi-innovation kalman filter algorithm which had higher convergence speed and estimation accuracy to reduce the large noise of the output information generated by sensors. The fault could be detected and the value of the pitch sensor bias could be estimated through the change of the tiny displacement. The simulation results show that the proposed approach is able to diagnose the pitch sensor bias fault effectively.

Recommended Citation

Zhai, Yanjie; Wu, Dinghui; Li, Yiyang; and Ji, Zhicheng (2020) "Research on Fault Diagnosis for Pitch Sensors of Wind Turbines," Journal of System Simulation: Vol. 27: Iss. 7, Article 7.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss7/7

First Page

1451

Revised Date

2014-10-13

DOI Link

https://doi.org/

Last Page

1457

CLC

TP391.9

Recommended Citation

Zhai Yanjie, Wu Dinghui, Li Yiyang, Ji Zhicheng. Research on Fault Diagnosis for Pitch Sensors of Wind Turbines[J]. Journal of System Simulation, 2015, 27(7): 1451-1457.