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

• Decision module of a diagnostic system based on ensemble of classifiers.
• Approach allows for incremental learning of new fault classes.
• Model-based residual generation ensures robustness w.r.t. operating point changes.
• Application to sensor fault diagnosis for a doubly fed induction generator.

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.