Improvement of condition monitoring (CM) systems for wind turbines (WTs) and reduction of the cost of wind energy are possible if knowledge about the condition of different WT components is available. CM based on the WT drive train shaft torque signal can give a better understanding of the gearbox failure mechanisms as well as provide a method for detecting mass imbalance and aerodynamic asymmetry. The major obstacle preventing the industrial application of CM based on the shaft torque signal is the costly measurement equipment which is impractical for long-term use on operating WTs. This paper suggests a novel approach for low-cost, indirect monitoring of the shaft torque from standard WT measurements. The shaft torque is estimated recursively from measurements of generator torque, high speed shaft and low speed shaft angular speeds using the well-known Kalman filter theory. The performance of the augmented Kalman filter with fading memory (AKFF) is compared with the augmented Kalman filter (AKF) using simulated data of the WT for different load conditions, measurement noise levels and WT fault scenarios. A multiple-model algorithm, based on a set of different Kalman filters, is designed for practical implementation of the shaft torque estimator. Its performance is validated for a scenario where there are frequent changes of operating points. The proposed cost-effective shaft torque estimator overcomes all major problems, which prevent the industrial application of CM systems based on shaft torque measurements. Future work will be focused on validating the method using experimental data and developing suitable signal processing algorithms for fault detection.

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