Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Abstract

As wind energy becomes one of the fastest growing renewable energy resources, the control of large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable-speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non-affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter-free controller. In addition, a high-gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed-loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

“…A block diagram of this strategy is presented in Figure 14. In [69], a method of pitch control is proposed, an ANN with an Online Learning Approach (OLA) is used. As an input variable, we have the error of the control variable; the error is previously filtered to transform a complex system into a simple one.…”

Iterative modeling of wind turbine power curve based on least‐square B‐spline approximation

“…WTs are shut down and the output active power is zero due to lack of wind energy. Region II : When wind speed is in the range of cut‐in value v cut _ in and rated value v rated , the main control objective is to harvest as much power as possible. The generated power P can be written as where λ = Rω / v is the tip speed ratio. Rotor speed ω is treated as main control variable in this region, while the pitch angle β is usually zero to maximize captured wind energy. Region III : If the wind speed increases above the rated value but still lower than cut‐out value v cut _ out , to guarantee the safe operation of WTs under high wind speed to avoid tremendous shock on components, the generated wind power is maintained at a rated value P rated , by actuating pitch angle with a certain pitching controller. Region IV : Wind speed exceeds the cut‐out value v cut _ out and WT is shut down for over speed limit protection in this region.…”

“…Region II: When wind speed is in the range of cut-in value v cut_in and rated value v rated , the main control objective is to harvest as much power as possible. The generated power P can be written as [4]…”

“…With the shortage of fossil energy resources, wind energy is recognized as the fastest growing renewable alternative due to its affordable, accessible and environmentally friendly characteristics . Along with the increasing improvement and popularity of wind turbines (WTs), the issues concerning optimization of operation and maintenance (O&M) such as intelligent control, performance assessment, condition monitoring (CM), fault detection, diagnosis and prognosis have drawn more and more attentions in recent years . Moreover, the supervisory control and data acquisition (SCADA) system integrated in WT gains widely growing popularity owing to its ability to collect and process huge amount of critical operating data in a real‐time manner, and thence provides sufficient source data sets for such research activities from data‐based perspective.…”

Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions

“…This controller is designed by employing a trade-off between pitch angle fluctuations and output power fluctuations. In [11], an adaptive neural pitch controller is proposed for the WTC problem. An online, two-layer neural network model is proposed for estimating the unknown wind turbine aerodynamics.…”