Incremental design of a decision system for residual evaluation: A wind turbine application

Document Type

Conference Proceeding

Publication Date

1-1-2012

Publication Title

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volume

8

Issue

PART 1

First Page

343

Keywords

Classifiers, Fault diagnosis, Sensor faults, State observers, Wind turbine

Last Page

348

Abstract

This paper presents an incremental way to design the decision module of a diagnostic system by resorting to dynamic weighting ensembles of classifiers. The method is applied for sensor fault detection and isolation in a doubly fed induction generator for a wind turbine application. A bank of observers generates a set of residuals. These signals are progressively fed into a dynamic weighting ensembles algorithm, called Learn++NC, for fault classification. The proposed algorithm incrementally learns the residuals-faults relationships and classifies the faults including multiple new classes, based on a dynamically weighted consult and vote mechanism that combines the outputs of the base-classifiers of the ensemble. © 2012 IFAC.

DOI

10.3182/20120829-3-MX-2028.00127

ISSN

14746670

ISBN

9783902823090

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