Fiona Dunne

A tutorial of wind turbine control for supporting grid frequency through active power control

As wind energy becomes a larger portion of the worlds energy portfolio and wind turbines become larger and more expensive, wind turbine control systems play an ever more prominent role in the design and deployment of wind turbines. The goals of traditional wind turbine control systems are maximizing energy production while protecting the wind turbine components. As more wind generation is installed there is an increasing interest in wind turbines actively controlling their power output in order to meet power setpoints and to participate in frequency regulation for the utility grid. This capability will be beneficial for grid operators, as it seems possible that wind turbines can be more effective at providing some of these services than traditional power plants. Furthermore, establishing an ancillary market for such regulation can be beneficial for wind plant owner/operators and manufacturers that provide such services. In this tutorial paper we provide an overview of basic wind turbine control systems and highlight recent industry trends and research in wind turbine control systems for grid integration and frequency stability.

Combining Standard Feedback Controllers with Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines

Combined feedback/feedforward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent winds. The feedforward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Various collective pitch and individual pitch versions of two feedforward designs are studied: Gain-Scheduled Model-Inverse and Gain-Scheduled Shaped Compensator. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by LIDAR. Three of the designs reduce structural loading compared to standard feedback control, without reducing power production.

Adding Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter Methods

Combined feedback/feedforward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent winds. The feedforward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Three feedforward designs are studied: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by lidar. Noncausal series expansion and Preview Control methods reduce blade root loads but increase tower bending in simulation results. The optimized FIR filter reduces loads overall, keeps pitch rates low, and maintains rotor speed regulation and power capture, while using imperfect wind measurements provided by a lidar model.

Benefit of wind turbine preview control as a function of measurement coherence and preview time

Wind turbine control is typically feedback only, relying on measurements from a generator-speed sensor, and sometimes strain gauges and accelerometers. Recently, lidar and other technologies that provide a preview measurement of the incoming wind speed are also becoming available. This raises a question: How much benefit do these preview measurements provide? In this paper, we focus on answering this question for wind turbine collective blade pitch control in above-rated wind speeds, with the objective of minimizing a cost function that includes both generator-speed error and blade pitch actuation. We assume the International Electrotechnical Commission (IEC) Kaimal wind spectrum and the National Renewable Energy Laboratory (NREL) 5-MW turbine model, linearized at various operating points (wind speeds). We then use ℌ2 synthesis to design an optimal combined feedforward/feedback controller that depends on both the amount of preview time available in the wind speed measurement, and the coherence between the wind measurement and the wind that is actually felt by the turbine. Finally, we show how the resulting closed-loop cost decreases as a function of measurement quality (coherence bandwidth) and preview time.We include the special case where measurement coherence equals zero, which is equivalent to no feedforward (feedback-only) control. Results show the benefit of wind turbine preview control as a function of measurement coherence and preview time.

Analysis of gain-scheduling implementation for the NREL 5-MW turbine blade pitch controller

Gain scheduling for the widely-used NREL 5-MW proportional-integral blade pitch controller can be implemented either by the original documented method (Integrate First): integrate the generator speed error before multiplying by the gain-scheduling correction factor, or the Multiply First method: multiply the generator speed error by the gain-scheduling correction factor, then integrate. The results of the Multiply First implementation are straightforward: the effective proportional and integral gains are simply the unscheduled gains multiplied by the value of the gain-scheduling function evaluated at the pitch operating point for the average wind speed. However, the original (Integrate First) implementation effectively reduces the proportional and integral gains significantly further than the Multiply First implementation, with the difference becoming more severe as wind speed increases. Integrating First results in effective gain reductions of 34% at 13 m/s and 41% at 18 m/s compared to Multiplying First. Further, this effective gain reduction disappears if the baseline pitch controller is augmented with a feedforward control signal whose average value is the pitch operating point. These effects are explained through analysis of a simplified version of the gain-scheduling feedback loop and verified through simulation. Simulation results also show that when effective gains are corrected to match each other, Integrating First improves performance compared to Multiplying First. This gain correction is not feasible for real-world implementation, but a simple change to the gain-scheduling function f(u) instead is feasible and is shown to have almost the same effect. Lifetime performance metrics for Integrating First with the new f(u) show a 4% reduction in tower base moment, a 2% reduction in blade root moment, a 4% reduction in RMS pitch rate, and a 10% reduction in RMS power error compared to Multiplying First.