Title

Imputation of Missing Data for Diagnosing Sensor Faults in a Wind Turbine

Document Type

Conference Proceeding

Publication Date

1-12-2016

Publication Title

Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

First Page

99

Keywords

fault diagnosis, missing data imputation, regression, sensor faults, wind turbine

Last Page

104

Abstract

One of the crucial requirements for the practical implementation of empirical diagnostic systems is the capability of handling missing data. This is done by resorting to missing data imputation techniques in a pre-processing module. The pre-processing module is a part of a previously developed diagnostic system which receives batches of residuals generated by a combined set of observers and progressively feeds the processed residuals to a fault classification module that incrementally learns the residuals-faults relations and dynamically classifies the faults including multiple new classes. The proposed method is tested with respect to sensor fault diagnosis of the incomplete scenarios in a doubly fed induction generator (DFIG) of a wind turbine.

DOI

10.1109/SMC.2015.30

ISBN

9781479986965

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