Wind turbine fault detection and isolation using support vector machine and a residual-based method

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.