Wind farm control strategies for structural load management and energy efficiency

Abstract

Wind turbines have been deployed worldwide to address the growing energy demand while also targeting ambitious plans for the energy transition to renewable sources. Although wind energy is a key renewable energy source, it still faces unsolved challenges and offers ample room for innovation. One major concern is its variability, with the maximum power available fluctuating over time. Importantly, wakes from upstream turbines also contribute to reducing the power availability for downstream turbines. These wake effects are magnified by the construction of large wind farms with densely spaced wind turbines, aiming to efficiently use the allocated space.

Wind farm control strategies can be implemented to mitigate these wake effects and optimize wind farm power generation. In scenarios requiring on-demand response, such as those explored in this thesis, wind turbines are leveraged to provide flexibility, constrained by their maximum power availability. The power delivery of wind power plants upon request is facilitated by a closed-loop wind farm controller, providing active power control at fast timescales. Active power control involves adjusting the resource's active power to assist power grid operators in balancing energy supply and demand, thereby improving energy security.
Our proposed closed-loop control solution provides superior response capabilities by
compensating for reduced power availability, ultimately enhancing the reliability of on-demand power generation.

The wind variability across turbines, intensified by wake effects, contributes not only to attaining fluctuations in power generation but also to fluctuations in structural loads on the turbines. Amplified by wake-induced turbulence, this structural load variability across turbines leads to uneven degradation of turbine components over the long term. In offshore scenarios, where accessibility is limited and maintenance operations must be minimized due to higher costs compared to onshore counterparts, controlling turbines to prolong their lifetime is of significant interest. In this thesis, this aspect is addressed at both the wind farm and wind turbine levels.

At the farm level, we propose that farms fulfilling grid energy demands must also balance the aerodynamic forces of their turbines to evenly distribute structural degradation among them.
This can be achieved without compromising the power generation when the turbines operate below their maximum energy extraction capacity. We have demonstrated that by implementing a real-time feedback loop, it is feasible to balance aerodynamic loads while meeting wind farm energy demands, albeit limited by wind availability. Moreover, we have demonstrated that balancing aerodynamic forces is advantageous for active power control in a wind farm affected by wake effects, compared to simply distributing power requests uniformly.

At the wind turbine level, we introduced two wind turbine controllers designed to individually restrict real-time aerodynamic loads as a surrogate of structural loads in turbine components. These controllers are referred to as load-limiting controllers. The first load-limiting controller employs an optimal control approach. The operator can impose structural load constraints, using a convex model predictive control for power tracking. The second controller, which is more practical, utilizes a switching mechanism with integral control that allows the operator to prioritize a structural load setpoint over a power demand setpoint. This prioritization aims to reinforce structural safety in situations where turbines are compromised from their design conditions. This could be a consequence of numerous factors, such as unpredictable degradation, installation issues, vessel collisions, and others.

As wind turbines prove to be a viable, reliable, and eco-friendly energy source, new wind farm projects are becoming more ambitious, incorporating a larger number of turbines than ever before. Additionally, there is a substantial growth in wind turbine installations within existing wind farms. This growth in the number of turbines poses an implementation challenge for wind farm control systems. Similar challenges have been encountered in controlling other large-scale systems with collective goals, where agents must instead make decisions based on partial information due to communication limitations in processing or transmission.

Anticipating this implementation challenge, we transition from a centralized to a distributed wind farm control solution. Taking advantage of the time scale inherent in typical wind farm controller implementations, we exchange information with neighboring turbines rather than a central workstation. Our aim, in particular, is not to gather partial information but to achieve consensus across the entire farm. However, our control methodology has a negative implication - the addition of delays - which is carefully examined by the derived stability condition for the design and is assessed through simulations. Notwithstanding these delays, the proposed solution is fully distributed and has been demonstrated to be both simple and effective, facilitating the application of our control solutions in large-scale wind farms.

Lastly, we validate our wind farm control solutions through experiments conducted with scaled wind turbines in full-wake conditions. In this way, we verify the benefits of our control solutions not only through high-fidelity simulations but also through real-world experimentation.

The work presented in this thesis emphasizes the importance of wind turbine controllers capable of offering demanded power to the grid while enhancing reliability in power delivery and addressing structural and maintenance concerns. We introduce closed-loop wind farm controllers designed to handle these challenges. Furthermore, we expand the implementation through a distributed approach on one front, while on the other front, we validate the solutions by means of experiments. The findings from this research contribute to the efficient operation of future wind farms by employing feedback control strategies across clusters of wind turbines.