Model-based Prognosis Approach using a Zonotopic Kalman Filter with Application to a Wind Turbine

Wind turbines generally operate under adverse conditions making them prone to relatively high failure rates. Due to the direct exposure of the blades to dynamic and cyclic loads of wind, the rotor and the blades unsurprisingly represent the most common major component damages of a wind turbine system, which is especially enhanced when located offshore. This paper presents a new model-based prognosis procedure based on a zonotopic Kalman filter (ZKF), which combines a physical model with observed data to assess the system degradation. Using this information and the model of the system, the end of life (EOL) and the remaining useful life (RUL) with its uncertainty can be predicted. The proposed prognostic method is applied to monitor the state of health of a wind turbine system specifically, its blades. The remaining useful life prediction will help in scheduling optimal maintenance and reducing the cost caused by wind turbine damage and unplanned shutdown.

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