With the rapid development of green energy, the efficiency and reliability of wind turbines are key to sustainable renewable energy production. For that reason, this paper presents a novel intelligent system architecture designed for the dynamic collection and real-time processing of visual data to detect defects in wind turbines. The system employs advanced algorithms within a distributed framework to enhance inspection accuracy and efficiency using unmanned aerial vehicles (UAVs) with integrated visual and thermal sensors. An experimental study conducted at the "Staryi Sambir-1" wind power plant in Ukraine demonstrates the system's effectiveness, showing a significant improvement in defect detection accuracy (up to 94%) and a reduction in inspection time per turbine (down to 1.5 hours) compared to traditional methods. The results show that the proposed intelligent system architecture provides a scalable and reliable solution for wind turbine maintenance, contributing to the durability and performance of renewable energy infrastructure.

翻译：随着绿色能源的快速发展，风力涡轮机的效率和可靠性是可持续可再生能源生产的关键。为此，本文提出了一种新颖的智能系统架构，旨在动态采集并实时处理视觉数据以检测风力涡轮机缺陷。该系统在分布式框架内采用先进算法，利用集成视觉与热传感器的无人机（UAV）来提升检测精度与效率。在乌克兰"Staryi Sambir-1"风电场进行的实验研究验证了该系统的有效性，与传统方法相比，缺陷检测准确率显著提升（最高达94%），单台涡轮机检测时间缩短至1.5小时。结果表明，所提出的智能系统架构为风力涡轮机维护提供了一个可扩展且可靠的解决方案，有助于提升可再生能源基础设施的耐久性与性能。