PV plant digital mapping for modules' defects detection by unmanned aerial vehicles. Issue 10 (30th May 2017)

• Record Type: Journal Article
• Authors: Grimaccia, Francesco; Leva, Sonia; Niccolai, Alessandro
• Is Part Of: IET renewable power generation. Volume 11:Issue 10(2017)
• Page Start: 1221
• Page End: 1228
• Publication Date: 2017-05-30
• Subjects: autonomous aerial vehicles -- photovoltaic power systems -- power generation faults -- electrical maintenance -- fault diagnosis -- power generation reliability -- image processing -- automatic optical inspection -- solar cells -- power system measurement; PV plant digital mapping -- modules defect detection -- unmanned aerial vehicles -- plant monitoring -- renewable energy sources -- image post‐processing tool -- remote aerial images -- photovoltaic defects detection -- PV defects detection -- maintenance operators -- light UAV -- PV systems -- system failures -- energy yield -- power plants -- fast fault detection -- automatic system; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2016.1041
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Nowadays plant monitoring and control of renewable energy sources can take advantage of the use of unmanned aerial technologies. This manuscript aims to develop an image post‐processing tool for remote aerial images able to help operation and maintenance operators in photovoltaic (PV) defects detection using light unmanned aerial vehicles (UAVs). In particular, PV systems deployed in the field in the last ten years show often critical behaviour with a range of failures able to compromise the performance and energy yield of the power plants, thus they can greatly benefit of such novel technologies and tools. The described procedures are here tested on real plant data, with different kind of sensors, in order to find out potential advantages in fast fault detection tasks, using an automatic system for PV plants' mapping. The results of this research will be reported in order to provide an understanding of potential impact of image processing techniques based on UAV in the renewable energy sector.