用无人机+分布式系统实时检测风机缺陷，效率提升显著。
Distributed Intelligent System Architecture for UAV-Assisted Monitoring of Wind Energy Infrastructure
- 基于无人机与分布式算法，融合视觉热成像数据
- 缺陷检测准确率达94%，单机巡检时间缩短至1.5小时
- 适合风电场智能运维，可扩展性强
随着绿色能源的快速发展，风力发电机的效率和可靠性对可持续可再生能源生产至关重要。本文提出一种新型智能系统架构，用于动态采集和实时处理视觉数据，以检测风力发电机缺陷。该系统在分布式框架中采用先进算法，利用搭载视觉与热成像传感器的无人机，提升检测精度与效率。在乌克兰“Staryi Sambir-1”风电场的实验表明，该系统将缺陷检测准确率提升至94%，单台风机巡检时间降至1.5小时，显著优于传统方法。结果表明，所提出的智能系统架构为风力发电机维护提供了可扩展、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。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。