Incremental Design of a Decision System for Residual Evaluation: a Wind Turbine Application

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.