Abstract
Monitoring and fault diagnosis plays a key role in improving the reliability, availability and productiveness of wind turbine systems. When a wind turbine is subjected to multiple faults, it is even more challenging to identify and classify the faults. In this paper, multi-linear principal component analysis (MPCA) is employed to extract the significant features of a wind turbine for the purpose of fault classification of multiple faults. Simulations and validations are performed in terms of faulty data sets generated by a 4.8-MW wind turbine benchmark system subjected to two actuator faults.
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