Bioseco Sp. z. o. o., Budowlanych 68, 80-298 Gdansk, Poland.
Department of Mathematics and Natural Sciences, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden.
Sensors (Basel). 2021 Jan 3;21(1):267. doi: 10.3390/s21010267.
Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird's detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system's desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.
风能作为一种清洁可再生能源，已经被人类利用了数个世纪。然而，近年来，随着风力涡轮机数量和规模的增加，它们对鸟类的影响变得令人担忧。研究人员估计，在美国每年有多达 50 万只鸟类因与风力涡轮机碰撞而死亡。本文提出了一种在风力发电场周围减轻鸟类死亡率的系统。该解决方案基于嵌入式分布式计算和物联网范例的立体视觉系统。在定义区域检测到鸟类后，决策系统会激活由光和声音威慑以及涡轮机关停程序组成的避碰例程。开发过程采用了用户驱动设计方法，以及组件选择和启发式调整的过程。该提案包括一种鸟类检测方法和定位程序。鸟类识别使用人工智能算法完成。使用固定翼无人机进行的验证测试和鸟类学家的验证观察证明了该系统检测翼展超过 1.5 米、至少 300 米的鸟类的可靠性。此外，还证实了该系统适合将检测到的鸟类的大小分类为小、中、大三种翼展类别之一。
