Interval-based fault diagnosis in wind turbines using structural analysis and gaussian process regression*

Abstract

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This paper presents a hybrid fault diagnosis approach for wind turbines that integrates structural analysis through Analytical Redundancy Relations (ARRs) with datadriven modeling using Gaussian Process Regression (GPR). The proposed method leverages the physical structure of the system to define input-output dependencies and trains GPR estimators on fault-free operational data to predict key subsystem outputs. Residuals are computed by comparing sensor measurements with GPR predictions, and faults are detected using a combination of interval-based thresholds and Cumulative Sum (CUSUM) control charts. The proposed approach is validated on a simulated 5-MW wind turbine benchmark model under realistic operating conditions. Various fault scenarios are injected in the pitch actuator, drivetrain, and generator subsystems. Results demonstrate the fault diagnosis accuracy, robustness, and early detection capability across diverse fault types.

This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00 ).

Peer Reviewed

Postprint (author's final draft)