„The guardian

High above the lush forest I soared; proud, majestic and free. My sleak brown, white & grey plumage glistened in the warm sun, contrasting against the sea of green below. The wind whispered through the leaves, and the melodious chirping of my fellows accompanied my flight. It was a picture of serenity, a world I knew intimately.

But today was different. I could feel it in the air. The forest was alive with an unusual hum, and vibrations reverberated through the trees. As I soared higher, the sounds grew more pronounced. The distant hum of construction that I got used to gave way to a new, unfamiliar mechanical rythm.

As I ventured closer to the origin of the peculiar disturbance, I understood that construction work on the gigantic human structure had just been completed. Its enormous blades were now in motion, slicing through my air. The odd sound and vibrations emanating from them were unlike anything I had ever encountered. I could feel that their power was massive.

It was at this moment that a remarkable change occurred. Unbeknownst to me, an intricate AI system equipped with advanced sensors was monitoring my flight. The sensors detected my approach and sent rapid signals to the towering wind turbines. In response, the turbines gracefully slowed down and came to a silent halt, ensuring that I remained unharmed.

Now, suspended in the midst of the stopped turbines, I was puzzled by the sudden silence. I cautiously landed on one of the turbine blades and observed my serene surroundings. The forest’s symphony had returned, uninterrupted by the mechanical giants that had only just begun their operation.

The source of this intervention remained a mystery, but I felt a deep sense of gratitude. I understood that there were unseen protectors watching over us, ensuring that our world remained in harmony. With a final glance, I resumed my solo journey, soaring into the sky with a newfound sense of optimism, as the towering turbine slowly started to turn again.“

The background

Bird Strikes and Wind Turbines: A Looming Challenge

Bird and bat mortality due to wind turbines have emerged as a growing concern in recent years as the global reliance on wind energy continues to rise. While wind turbines are esteemed a major pillar in the transformation towards renewable energy, they pose a significant risk to avian populations, especially during migration periods and in areas where important bird corridors intersect with wind energy parks [A, B].

Bird strikes occur when birds inadvertently collide with the rotating blades of wind turbines. These collisions result in severe injuries or fatalities, contributing to declines in bird populations, ecological imbalances, and an ethical dilemma for the renewable energy industry. Addressing this problem is crucial to maintain the delicate balance between renewable energy production and the preservation of wildlife.

AI-Assisted Approaches for Mitigation

Thankfully, innovative solutions involving AI technology are being developed to mitigate bird strikes and reduce avian mortality around wind turbines:

1. Radar-Based Systems: advanced radar systems are employed to detect birds approaching wind turbines. AI algorithms can analyze radar data in real-time and trigger immediate shutdown of turbines when birds are detected within a certain proximity. This proactive approach significantly reduces the risk of collisions. [C]
2. Thermal Imaging and Computer Vision: in a similar vein, thermal imaging and computer vision systems are used to identify birds in the vicinity of wind turbines. AI algorithms process thermal data and camera feeds, distinguishing between birds and other objects. When birds are identified near the turbines, a shutdown command can be issued. Current AI systems are able to detect and classify birds correctly in 77 – 85% of all cases [D].
3. Acoustic Deterrence: AI-powered acoustic sensors can detect bird vocalizations and flight patterns. When birds approach the danger zone, AI systems emit high-frequency sounds or deterrent noises that discourage them from getting too close to the turbines.
4. Migration prediction: machine learning models are trained on historical data to predict bird migration patterns and identify high-risk periods for bird strikes. These models help operators proactively reduce turbine rotation speeds or temporarily shut down turbines during peak migration times. [E]
5. Habitat Assessment: AI-assisted ecological studies use satellite imagery and sensor data to assess the proximity of wind energy facilities to important bird habitats. This information aids in responsible siting of wind turbines, minimizing the overlap with critical bird corridors. [F]

Incorporating AI-assisted solutions in wind energy facilities offers a promising path towards reducing bird strikes and protecting avian populations [G]. These technologies not only enhance the sustainability of wind energy but also contribute to a harmonious coexistence between renewable energy production and the preservation of the natural world. As these AI systems continue to evolve, they hold the potential to make wind energy even more bird-friendly while mitigating the ecological impact of renewable energy sources.

References:

[A] Rydell, J., H. Engstrom, A. Hedenstrom, J. K. Larsen, J. Pettersson, and M. Green, The Effect of
Wind Power on Birds and Bats | A Synthesis, 6511, Swedish Environmental Protection Agency,
2012.

[B] Grunkorn, T., J. Blew, T. Coppack, O. Kruger, G. Nehls, A. Potiek, M. Reichenbach, J. von
Ronn, H. Timmermann, and S. Weitekamp, Ermittlung Der Kollisionsraten von (Greif )Vogeln Und
Schaffung Planungsbezogener Grundlagen Fur Die Prognose Und Bewertung Des Kollisionsrisikos
Durch Windenergieanlagen (PROGRESS). Schlussbericht Zum Durch Das Bundesministerium
Fur Wirtschaft Und Energie (BMWi) Im Rahmen Des 6. Energieforschungsprogrammes Der
Bundesregierung Geforderten Verbundvorhaben PROGRESS, FKZ 0325300A-D, 2016.

[C] Zadeh, Ashkan Taremi, et al. „Towards localization and classification of birds and bats in windparks using multiple FMCW-radars at Ka-band.“ Progress In Electromagnetics Research M 109 (2022): 1-12.

[D] Duerr, Adam E., et al. „Effectiveness of an artificial intelligence-based system to curtail wind turbines to reduce eagle collisions.“ Plos one 18.1 (2023): e0278754.

[E] Kranstauber, B., Bouten, W., van Gasteren, H., & Shamoun-Baranes, J. (2022). Ensemble predictions are essential for accurate bird migration forecasts for conservation and flight safety. Ecological Solutions and Evidence, 00, 3, e12158. https://doi.org/10.1002/2688-8319.12158

[F] Salkanović, Eldina. „Protecting avian wildlife for wind farm siting: The Screening Tool Proof of Concept.“ Energy for Sustainable Development 74 (2023): 66-78.

[G] M. Principato, L. Hasselwander, M. Stangner and R. Buettner, „Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action“ in IEEE Access, vol. 11, pp. 64026-64048, 2023, doi: 10.1109/ACCESS.2023.3287861.