Wind Turbine Fault Detection and Isolation Using Support Vector Machine and a Residual-Based Method

Authors

Jianwu Zeng, University of Nebraska-LincolnFollow
Dingguo Lu, University of Nebraska-LincolnFollow
Yue Zhao, University of Nebraska-Lincoln
Zhe Zhang, University of Nebraska-Lincoln
Wei Qiao, University of Nebraska-LincolnFollow
Xiang Gong, University of Nebraska-Lincoln

Date of this Version

2013

Citation

2013 American Control Conference (ACC) Washington, DC, USA, June 17-19, 2013

Comments

© 2013 AACC

Abstract

This paper proposes a novel scheme combining support vector machines (SVM) and a residual-based method for wind turbine fault detection and isolation (FDI). SVMs with radius basis function kernels are used for detecting and identifying sensor stuck and offset faults, where binary codes of fault types are used as the outputs of the SVMs to minimize the number of SVMs being used. The same output of a SVM may correspond to different types of faults and the final decision is made by all SVMs instead of one SVM. Moreover, a residual-based fault detection method using a time-variant threshold is developed to identify the abrupt change and scaling faults. Monte Carlo simulations are carried out in MATLAB to test the effectiveness and robustness of the proposed FDI methods using a wind turbine FDI benchmark model. Results show that the proposed methods can always detect the faults successfully within the required time limits.