Predictive control of wind turbines by considering wind speed forecasting techniques

Fixed speed wind turbines have low efficiency as compared to variable-speed, fixed-pitch wind turbines. The latter are required to optimize power output performance without the aerodynamic controls. A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind rotor at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. Therefore, a special emphasis is given to operating at maximum aerodynamic power points of the wind rotor. In this study, wind speed forecasting techniques are considered for predictive optimum control system of wind turbines to reduce response time of the MPPT controller.

I. Introduction

Wind speeds continuously varies and although wind rotor is required to drive at an optimal rotor speed for a particular wind speed, wind rotor speed can not be instantaneously changed. Therefore, the response of the wind rotor to wind speed variation affects the performance of the system. Wind speed time-series data typically exhibit autocorrelation, which can be defined as the degree of dependence on preceding values[1]. Autocorrelated time series models are usually used for wind speed prediction. In an autocorrelated wind speed-time series, the value of wind speed in anyone time step is strongly influenced by the values in previous time steps. Therefore, in this study wind speed prediction techniques are applied to improve the response of wind rotor speed variation and energy capture.