Load reduction based on a stochastic disturbance observer for a 5 MW IPC wind turbine

摘要

Control and operation systems of wind turbines must primarily ensure the fully automatic operation of wind turbines in a constantly changing environment. Economic efficiency charges the control system to ensure that the highest possible efficiency is achieved and the mechanical loads caused by disturbances are minimized. The ability of an observer, in this case a Kalman filter (Kf), to estimate non-measurable states from a set of measurements using a model of the plant suggests the idea of extending the model of the plant by a model of the disturbance. Disturbance states thus can be reconstructed and an easy-to-determine quasi-disturbance-feedforward controller can be used to reject them. This method is called Disturbance-Accommodating Control (DAC). In this paper, Dryden's turbulence model-which shapes a white noise signal via a form filter to meet spectrum conditions - and an inverse notch filter to model the rotational sampling effect are used for each blade, in contrary to the hitherto used deterministic disturbance models or the simple random walk models for stochastic turbulence. Measurement- and model-uncertainties are described as uncorrelated white noise. With this approach, the requirements of the Kf derivation are met and quantitative measures for the Kf process noise covariance matrix are available especially for the disturbance. The simplified tuning process and the high potential for load reduction are demonstrated for the NREL 5 MW Wind turbine. The reduction by a factor of 4.4 of the standard deviation of the flapwise root bending moment shows the high potential of this stochastic DAC approach. A parameter study to determine the influence of the turbulence spectrum bandwidth and to identify the dependency of the stochastic DAC approach on uncertainties of the process noise covariance matrix was performed. The study shows that the Kf is robust against a wide spectrum of parameter variations. Only if the time constant of the Dryden filter is significantly reduced, the performance is decreased.