5.3.2 TRANSECTA DE VERANO DE

Appropriate wind farm operation has the benefits of better grid integration, lower maintenance costs, and more energy production [13]. For controls of turbines in wind farm operation, there are also two aspects similar to the stand-alone: energy capture and

load reduction. However, under wind farm operation, both aspects present different challenges than the stand-alone turbine operation.

To the author’s best knowledge, the traditional control strategies for wind farm operation emphasize the effect of the deficit of average wind speed, i.e. on how to guarantee the power quality for grid integration by the control of the electrical systems [14, 15] or maximize the energy capture of the whole wind farm [16]. However, wind farm control strategies for maximizing energy capture is still far from mature due to complex wake phenomenon. From another standpoint, it is obvious that the asymmetric nature of wake interaction would bring great impact on structural load. In this study, both load reduction control and maximizing energy capture control are investigated.

1.3.1. Load Reduction Control for Turbines in Farm Operation

For stand-alone wind turbines, controls for energy capture is generally based on mean wind speed (e.g. hub height), while controls for load reduction is concerned more with the asymmetry within the rotor disc. For stand-alone turbines, the incoming wind speed is generally uniform except for the vertical wind shear due to the atmospheric boundary layer (ABL), shown in Fig. 1.5.

Fig. 1.5 Atmospheric Boundary Layer

For turbines in wind farm operation, however, the downstream turbines are exposed to a different situation. After passing the upstream turbines, the wind speed is determined by the wake characteristics. Thus, the downstream turbines have non-uniform wind distribution within the rotor disc due to the overlap of the wake of the upstream turbines, as shown in Fig. 1.6.

Fig. 1.6 Wake Overlap at Downstream Turbines Tower Ground Atmospheric Boundary Layer Wind Speed

Wind turbine wake models play a critical role for wind farm control because wake interaction significantly influences both energy capture and loads of the downstream turbine. In wind farm, average wind speed at the downstream turbine can be predicted by use of simple wake models, such as Jensen wake model [17], which are accurate enough for energy capture calculation. However, wind speed across the whole rotor plane is necessary for load calculation of the downstream wind turbine.

Even worse, the actual wake behavior is not static, i.e. the wind turbine wakes actually move bodily in lateral direction in wind farm. This is the so-called wake meandering [18] phenomenon. Wake meandering produces time-varying loading on the downstream wind turbines. Therefore, incorporation of wake meandering model is beneficial for better load reduction control of downstream turbines.

Structural load reduction in the context of wind farm operation was regarded as an opportunity which had not been investigated due to the complexity in predicting the wind speed over the rotor disc of the downstream turbine [13]. In order to achieve better load reduction control for farm operated wind turbines, more accurate wake models are needed to accurately predict wind speed across rotor plane at downstream wind turbines [13]. Based on the above issues, more accurate wake models including wake interaction and wake meandering were built and corresponding controllers were designed for load reduction control of wind turbine in farm operation.

When wake meandering happens, downstream wind turbine dynamics is nonlinear due to varying wind conditions. It is easier to obtain multiple linearized wind turbine dynamic models rather than explicit nonlinear wind turbine dynamic models under wake meandering. At the same time, model predictive control (MPC) [19] is good at

systematically dealing with constraints which are important for wind turbine control, such as limits of blade pitch angle and rate. In this situation, one kind of nonlinear model predictive control, multi-model predictive control (MMPC) [20], was chosen for loads reduction control of wind turbines under wake meandering.

1.3.2. Energy Capture Control in Wind Farm Level

The energy capture control of wind farm has the key difference from that for a stand- alone wind turbine: maximizing the energy capture of individual turbines does not lead to maximizing energy capture of a wind farm due to the velocity deficit and wake interaction. Intuitively speaking, for a wind farm, an upstream turbine should rotate somehow slower than its optimum speed in stand-alone operation, thus extracting less kinetic energy so that more energy may be extracted by the downstream turbines, which eventually increases the total energy capture of a wind farm [21]. There is an interesting observation that the fatigue loads was reduced when energy capture of a cascade of turbines was enhanced [21]. Although the optimal induction factors were obtained for a cascaded array of wind turbines [21, 22], it is difficult to implement wind farm control by use of optimal induction factors.

Model-based control strategies, such as model predictive control [23] and numerical optimization [24], also had been used for wind farm control for maximizing energy capture. The issue for model-based control of wind farm is that wake models may be accurate for flat terrain but inaccurate for complex terrain. Therefore, self-learning or self-optimizing approaches are received as more feasible solutions. Johnson and Thomas [16] proposed a hybrid approach for maximizing the wind farm energy capture by combining the Iterative Learning Control (ILC) and Iterative Feedback Tuning (IFT).

Marden et al. [25] proposed a model-free control strategy by use of game theory and cooperative control to optimize the axial induction factors to maximize power production of wind farm.

More recently, one wind farm control strategy had been patented by use of self- optimizing controller to maximize wind farm power output [22]. Its key idea is: the self- optimizing controller for an upstream turbine should be configured to control the upstream turbine in an attempt to maximize the combined total power output of this upstream turbine and downstream turbines in the wake of this upstream turbine.A better choice for self-optimizing controller is ESC. In this thesis, the nest-looped extremum seeking control (NLESC) scheme [22] was investigated for maximizing the wind farm energy capture.

1.3.3. Summary of Load Reduction and Energy Capture Control of Farm Operated Turbines

This dissertation study investigates both the load reduction control and energy capture control in wind farm level. First, the individual pitch control (IPC) is designed for load reduction to handle the wind variation due to wake interaction via a periodic control scheme. Then, to deal with the wake meandering phenomenon, a model predictive control (MPC) scheme is developed for the IPC of the downstream turbine loads. Thirdly, a novel Nested-Loop Extremum Seeking Control (NLESC) strategy is used to maximize energy capture of a wind farm.