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The future of control systems in wind energy

Where next for wind turbine control?

The control of wind turbines is not a new endeavour, nor is it niche. In fact, as early as 1990, over 70 published references were available on the various aspects of wind turbine control [1]. Now, a little over 25 years later, you would be hard pressed to keep up with every development in the field. The simultaneous rise in society’s focus on renewable energy sources and the proliferation of modern control methods have made solutions to wind turbine control problems academically attractive and commercially necessary. As the ability to exploit modern control techniques has progressed, the industry has responded quickly in assimilating them. The result of this is that controllers for modern wind turbines allow for previously impossible wind turbine designs and continue to force down the cost of energy from wind [2]. Back in 2007, who would have thought that a 126 metre rotor on a 3MW wind turbine was an economic choice? [3]

Improving turbine performance through its controller is typically catalysed by an increased understanding of the machine or its environment. For example, the advent of high fidelity modelling tools has empowered engineers to design controllers that specifically allow the damping of structural resonances in the turbine [4]. The availability of nacelle mounted LiDAR has enabled the design of controllers to exploit future wind speed information [5]. Although the balance of performance improvement versus robustness must be navigated carefully, the future of wind turbine control is closely coupled with how well we understand the turbine, its interaction with the environment and in a broader sense, its place in the energy landscape.

Looking at it from the individual turbine perspective; traditional control methods often have ‘patches’ applied to solve situations that are outside the norm. A lot of ‘do this, but if that happens then do this other thing, but only if this third thing is also happening…’; is quite exhausting! Now imagine a controller that commands the pitch and torque jointly, so that fatigue loads are reduced and energy capture is maximised under normal conditions. If the wind conditions become extreme, the controller knows that it needs to become more aggressive to meet its objectives and knows how to trade off the remaining fatigue life, against annual energy production. Furthermore, no specific scenarios have been pre-computed! Tools already exist that allow us to simulate a virtual wind turbine within the control system that can be used to optimise how the turbine functions [6]. We give the controller:

  • an objective: ‘make as much power as possible but keep the loads within bounds’;
  • give it an understanding of how control actions will affect the behaviour of the wind turbine in the near future;
  • let it know the bounds of the turbine’s abilities;

and then let it work out how best to achieve its goals.

Not only are patches unnecessary, but as we gain access to higher computational power, more efficient algorithms, a wider gamut of sensors and a deeper understanding of the wind turbine itself, we can include this new information into the modelling and prediction process and the controller’s objectives.

While the control of individual turbines is a key driver in turbine designs, the control of a wind turbine in relation to its place in the energy landscape has become a new target. The industry is now embracing a holistic view of an entire wind farm, as a single power plant and how each member of the power plant cooperates for the best performance as a unit. Significant effort is being placed on determining the best ways of controlling turbines, so that energy capture is maximised and fatigue loads are minimised across a wind farm, based on the instantaneous environmental conditions [7]. Enabled by the growing knowledge-base of turbine-wake interactions[8], the approaches show potential step changes in wind farm production and lifetime fatigue loading.

Controlling the wind farm itself can also assist the performance of the wider grid that it is connected to. Rightly or wrongly, it is not uncommon to hear about how distributed generation can destabilise an otherwise solid grid. But exploiting each modern wind turbine’s variable speed power converter allows a wind farm to actually provide grid support functionality [9]. By understanding grid connection characteristics, the grid performance requirements and the site specific environmental conditions, the turbine and wind farm control systems can provide valuable grid support services.

So where next for wind turbine control? The future is being shaped by reducing uncertainty: in our understanding of the turbine, its interaction with the environment and its interconnection to the wider energy landscape. Control systems that explicitly embrace this will continue to push the boundaries of what is possible. The goals may not change, but the performance will.


[1]         S. A. De La Salle, D. Reardon, W. E. Leithead, and M. J. Grimble, “Review of wind turbine control,” Int. J. Control, vol. 52, no. 6, pp. 1295–1310, 1990.

[2]         J. Dobbin, A. Cordle, L. Blonk, D. Langston, A. Kumar, E. Bossanyi, F. Vanni, and M. De Batista, “Fully Integrated Design: Lifetime Cost of Energy Reduction for Offshore Wind,” in Proceedings of the Twenty-fourth (2014) International Ocean and Polar Engineering Conference, 2014, vol. 3, pp. 415–423.

[3]         M. Del Franco, “Meet the Wind Turbines That ‘Fight Above Their Weight Class,’” North Am. Wind Power, vol. 11, no. 13, 2015.

[4]         E. A. Bossanyi, “Wind turbine control for load reduction,” Wind Energy, vol. 6, no. 3, pp. 229–244, 2003.

[5]         A. A. Kumar, E. Bossanyi, A. K. Scholbrock, P. A. Fleming, and M. Boquet, “Field Testing of LIDAR Assisted Feedforward Control Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600 kW Wind Turbine,” in EWEA, 2015.

[6]         A. A. Kumar, P. J. Rainey, and E. A. Bossanyi, “LIDAR Assisted Model Predictive Control of a Next Generation Wind Turbine for Tower Fatigue Load Reduction and Improved Speed Control,” in EWEA, 2015.

[7]         P. M. O. Gebraad, J. J. Thomas, A. Ning, P. A. Fleming, and K. Dykes, “Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control,” Wind Energy, 2016.

[8]         B. Naughton and T. Herges, “Sandia Wake Imaging System Field Test Report : 2015 Deployment at the Scaled Wind Farm Technology ( SWiFT ) Facility. SAND2015-8854,” Albuquerque, 2015.

[9]         J. Aho, A. Buckspan, and J. H. Laks, “A tutorial of wind turbine control for supporting grid frequency through active power control,” in Proc. of the American Control Conference, 2012, no. March, pp. 3120–3131.

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