A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines
Control Engineering Practice
Doubly-fed induction generator, Dynamic weighting ensembles, Fault diagnosis, Multiple observers, New class faults, Wind turbine
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.
Razavi-Far, Roozbeh and Kinnaert, Michel. (2013). A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines. Control Engineering Practice, 21 (9), 1165-1177.