Tsellou Aikaterini, Livanos George, Ramnalis Dimitris, Polychronos Vassilis, Plokamakis Georgios, Zervakis Michalis, Moirogiorgou Konstantia
School of Electrical and Computer Engineering (ECE), Technical University of Crete, 73100 Chania, Greece.
GeoSense, 57013 Thessaloniki, Greece.
Sensors (Basel). 2023 Oct 13;23(20):8441. doi: 10.3390/s23208441.
Power line inspection is one important task performed by electricity distribution network operators worldwide. It is part of the equipment maintenance for such companies and forms a crucial procedure since it can provide diagnostics and prognostics about the condition of the power line network. Furthermore, it helps with effective decision making in the case of fault detection. Nowadays, the inspection of power lines is performed either using human operators that scan the network on foot and search for obvious faults, or using unmanned aerial vehicles (UAVs) and/or helicopters equipped with camera sensors capable of recording videos of the power line network equipment, which are then inspected by human operators offline. In this study, we propose an autonomous, intelligent inspection system for power lines, which is equipped with camera sensors operating in the visual (Red-Green-Blue (RGB) imaging) and infrared (thermal imaging) spectrums, capable of providing real-time alerts about the condition of power lines. The very first step in power line monitoring is identifying and segmenting them from the background, which constitutes the principal goal of the presented study. The identification of power lines is accomplished through an innovative hybrid approach that combines RGB and thermal data-processing methods under a custom-made drone platform, providing an automated tool for in situ analyses not only in offline mode. In this direction, the human operator role is limited to the flight-planning and control operations of the UAV. The benefits of using such an intelligent UAV system are many, mostly related to the timely and accurate detection of possible faults, along with the side benefits of personnel safety and reduced operational costs.
电力线路巡检是全球配电网络运营商执行的一项重要任务。它是这些公司设备维护工作的一部分，并且是一个关键程序，因为它可以提供有关电力线路网络状况的诊断和预测。此外，在故障检测时，它有助于做出有效的决策。如今，电力线路巡检要么使用人工操作员徒步扫描网络并查找明显故障，要么使用配备能够录制电力线路网络设备视频的摄像头传感器的无人机（UAV）和/或直升机，然后由人工操作员离线检查这些视频。在本研究中，我们提出了一种用于电力线路的自主智能巡检系统，该系统配备了在可见光（红绿蓝（RGB）成像）和红外（热成像）光谱中运行的摄像头传感器，能够提供有关电力线路状况的实时警报。电力线路监测的第一步是将它们从背景中识别和分割出来，这是本研究的主要目标。电力线路的识别是通过一种创新的混合方法完成的，该方法在定制的无人机平台下结合了RGB和热数据处理方法，不仅在离线模式下提供了一种用于现场分析的自动化工具。在这个方向上，人工操作员的角色仅限于无人机的飞行计划和控制操作。使用这种智能无人机系统的好处很多，主要与及时准确地检测可能的故障有关，同时还有人员安全和降低运营成本等附带好处。
