For most power grid operators, it is challenging to have an accurate and up to date overview of the condition of their grid, equipment, and power lines. In fault situations and for preventive and corrective maintenance of power grid infrastructure, power grid companies have traditionally relied on manual ground based inspections (crews walking the lines) and helicopter based inspections. Recently the use of RPAS (Remote Piloted Aircraft Systems), popularly called drones, has started to be considered by several grid operators as a low cost and low risk alternative to helicopter inspections, and as an additional support to ground crews, greatly reducing the need for them to climb up power pylons with the associated HSE risks. This industry PhD project aims at investigating the use of the new generation of information technologies based on big data, machine learning, and real-time processing, to support the analysis of data acquired through RPAS based inspections. One of the main sources of data from RPAS inspections will be still images and video. Other sources of information, such as power-line noise measurements, IR (infra-red) imaging, multispectral and hyperspectral imaging, and LIDAR (light detection and ranging), are being evaluated for their suitability, and might be included later on in the scope of this project. The industry PhD project will primarily focus on image analysis and object recognition. The results of the PhD will be used directly in an ongoing R&D project at eSmart Systems aiming at developing a system to support and as much as possible automate infrastructure inspections for power grid companies.
Project leader: Davide Roverso
Institution: ESMART SYSTEMS AS