Efficient residuals pre-processing for diagnosing multi-class faults in a doubly fed induction generator, under missing data scenarios

This paper focuses on the development of a pre-processing module to generate the latent residuals for sensor fault diagnosis in a doubly fed induction generator of a wind turbine. The pre-processing module bridges a gap between the residual generation and decision modules. The inputs of the pre-processing module are batches of residuals generated by a combined set of observers that are robust to operating point changes. The outputs of the pre-processing module are the latent residuals which are progressively fed into the decision module, a dynamic weighting ensemble of fault classifiers that incrementally learns the residuals-faults relationships and dynamically classifies the faults including multiple new classes. The pre-processing module consists of the Wold cross-validation algorithm along with the non-linear iterative partial least squares (NIPALS) that projects the residual to the new feature space, extracts the latent information among the residuals and estimates the optimal number of principal components to form the latent residuals. Simulation results confirm the effectiveness of this approach, even in the incomplete scenarios, i.e., the missing data in the batches of generated residuals due to sensor failures