SKARV: Active wind turbine control for bird strike prevention

Expanding wind power generation will greatly contribute to our ability to meet net-zero emission targets. However, this expansion must be done in a way that avoids negatively impacting wildlife. The main goal of SKARV is to minimise wind energy’s negative impact on bird populations. The project proposes an operational mitigation strategy to reduce bird collisions with turbine blades. The aim is to develop a control system that actively modifies the speed of wind turbines when a bird is approaching. This will prevent collisions between blades and birds without shutting down the turbines. To do this, the bird’s trajectory is tracked for long enough and its future location is predicted. The wind turbine speed is then modified accordingly to minimise the probability of a collision. SKARV will provide the scientific basis and a simulation-based demonstration for the proposed mitigation solution. The project will adopt an interdisciplinary approach to derive the required algorithms for predicting bird trajectories and controlling the wind turbine speed. The results will include insight into the bird species that are most at risk of a collision with turbine blades and could be positively impacted by the collision avoidance control system. The project draws from disciplines within both engineering and bird ecology. In the long term, SKARV will facilitate the large-scale deployment of wind energy with reduced ecological impacts, which will contribute directly to achieving society’s climate goals.

SKARV aims to reduce collisions between birds and wind turbine blades by actively controlling the rotational speed of the turbine. This is a novel concept for minimising the negative impact of wind turbines on bird populations. Current post-construction minimisation measures to reduce bird collisions with wind turbines involve deterrents such as sounds and lights, and turbine curtailment. SKARV will be entirely imperceptible. The proposed solution minimises power production losses as well as disturbances to birds and biodiversity around wind farms. To enable this, several R&D challenges have to be addressed. For instance, the key challenge is how to derive a practical optimal control algorithm, which implements the collision-avoidance strategy in real-time, while limiting dynamic loads on the turbine structure and considering physical constraints of the system. The collision-avoidance strategy also requires the ability to predict the probability distribution of the bird's flight path ahead of time, based on current information about the bird altitude, position and velocity. Functional mitigation measures should be tailored to as many bird species as possible. However, this is a challenge, as bird species present different behaviour and morphology. Thus, it is important to understand the bird species that are at most risk from a collision with turbine blades, and to verify the control system behaviour for different flight patterns. This project will adopt an interdisciplinary approach to develop and evaluate the feasibility of the proposed control system, and conduct a dedicated study to reveal bird species that could be positively impacted by the control concept.