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
Recommended Citation
Razavi-Far, Roozbeh and Saif, Mehrdad. (2016). Imputation of Missing Data for Diagnosing Sensor Faults in a Wind Turbine. Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 99-104.
https://scholar.uwindsor.ca/electricalengpub/162