Adaptive Control of Wind Turbine Systems with Time-Varying Parameters

Zhang, Chi, Electrical Engineering - School of Engineering and Applied Science, University of Virginia
Tao, Gang, Department of Electrical and Computer Engineering, University of Virginia

The vibration problem on wind turbine blades is, at times, unavoidable. The
unpredictable effects of blade vibrations on the modern high-capacity and large size wind turbine can harm the efficiency of wind power generation, as well as the safety operation on the wind farm. The objective of this research is to deal with the aeroelastic dynamics of the spinning blade with dynamic load, which means we need to deal with the time-varying parameters in the wind turbine system model. The system nonlinearities could limit the performance of the feedback control.
In this research, we develop an adaptive backstepping control design for single-
input and single-output wind turbine dynamic systems with the control input as
the gurney flap which is deployed in the trailing-edge of the wind turbine blade. Our control scheme has the capacity to guarantee the desired system tracking performance in the presence of blade vibrations causing system parameters to be time-varying.
The model of an NACA0012 airfoil is presented for situations where two scenarios are studied: the nominal model with constant parameters and the actual model with time-varying parameters. The wind turbine dynamic system model used in our study is based on data taken from this airfoil model. We will first demonstrate with simulation results that an existing adaptive model reference control design can handle the scenario of the nominal model with constant parameters, but cannot achieve the desired system performance for the actual model with the time-varying system parameters. We will then show by both an analytic and simulation results that our developed adaptive backstepping control design effectively can handle the time-varying parameters, ensuring tracking the desired system output (pitch angle), despite large system parameter variations.

MS (Master of Science)
All rights reserved (no additional license for public reuse)
Issued Date: