A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines

Document Type

Article

Publication Date

9-1-2013

Publication Title

Control Engineering Practice

Volume

21

Issue

9

First Page

1165

Keywords

Doubly-fed induction generator, Dynamic weighting ensembles, Fault diagnosis, Multiple observers, New class faults, Wind turbine

Last Page

1177

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 wind turbine application. Three sets of observers are combined to generate residuals that are robust to operating point changes. These signals are progressively fed into a dynamic weighting ensembles algorithm, called Learn++.NC, for fault classification. The algorithm incrementally learns the residuals-faults relationships and dynamically classifies the faults including multiple new classes. It resorts to a dynamically weighted consult and vote mechanism to combine the outputs of the base-classifiers. © 2013 Elsevier Ltd.

DOI

10.1016/j.conengprac.2013.05.005

ISSN

09670661

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