SunMap: Towards Unattended Maintenance of Photovoltaic Plants Using Drone Photogrammetry

Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer's standard electrical tests.

Automated summary

Global interest in renewable energy resources, such as solar energy, has led to the development of new approaches for inspecting and monitoring PV plants. This paper presents a drone photogrammetry approach using RGB and IRT images to detect hot spots in PV plants. The methodology incorporates advances in photogrammetry and computer vision, resulting in an in-house software application called SunMap that provides automatic and accurate detection of hot spots and generates high-quality cartographic products.