Cost-Effective Flight Strategy for Aerial Thermography Inspection of Photovoltaic Power Plants

Abstract

Thermography from unmanned aerial vehicles (UAV) is widely used for module condition surveys and defect detection in solar (photovoltaic) power plants. This article presents an optimized defect inspection procedure in which a two-stage autonomous flight strategy is adopted to reduce the operation time, and thereby the cost. The first stage is a fast high-altitude flight for rapid coverage of the entire plant. The resolution on the modules is then somewhat degraded, but adequate for detection of possible defect locations. In the second stage, an optimized flight path is calculated to revisit and image only the detected locations from a lower altitude, where the resolution is sufficient for the classification of defects. This concept is studied through simulations of different plant geometries and defect densities. The simulations are supported by actual data, including a plant-scale survey. The proposed strategy is shown to have the potential for significant savings in operation time, on the order of 60% in the experimental case with 2% module defects. Commonly reported defect densities of 0.5% to 1% will give even larger savings. The inspection strategy is shown to be especially beneficial for plant geometries representative of high latitudes.

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

“…Concluding, the use of UAV diagnostic systems can improve the overall O&M procedures and reduce cost (O&M and LCOE) by increasing the inspection efficiency up to 97 % on average compared to the manual inspection [ 26 ]. Furthermore, autonomous flight strategies were found to further reduce the operation time of a UAV by approximately 60 % [ 64 ]. The development of accurate fault diagnosis procedures could avoid economic losses up to 7.4 million $/year for an 1 GW p PV plant (with approximately 4 % losses in energy yield) [ 30 ].…”

“…The thermal cameras measure the PV module surface temperature and produce an image (called a thermography or thermogram) with colours, that can be easily interpreted, showing thermal patterns and behaviour. IRT is a very effective inspection tool since the defected PV modules usually experience deviation in their temperature distribution [ 64 ]. Thermal imaging is usually performed during daylight hours with minimum radiation levels [ 44 ].…”

“…UAVs provide various benefits and unique opportunities for field PV applications. This can be attributed to the latest developments in aerial technology, sensors, and control systems which support UAV and make them an appropriate tool for inspecting and monitoring systems [ 64 ]. Extensive coverage, exceptional flexibility, low weight, autonomous operation and fast speed can be achieved when using UAV-based systems [ 76 ].…”

“…However, some challenges may arise when developing such systems, deteriorating the effectiveness and the autonomy of UAV's. Such challenges are concerned with the localization of the drone (where the precision of the geolocalization is not sufficient to allow flying at low altitudes), autonomous flight duration and flying paths over the plant to avoid overlap and empty areas between the PV strings, acquired images resolution and quantification of the impact of each fault's pattern on PV performance degradation [ 64 , 72 , 115 , [134] , [135] , [136] , [137] , [138] ]. Towards tackling these challenges, vision-based control laws were suggested to track PV panel rows based on PV modules' edge detection [ 134 , 136 , 139 ], while different techniques were also proposed to extract the plant's boundary via computer vision techniques and compute the UAV path over the plant [ 135 , 138 ].…”

“…In this context, Lofstad-Lie et al [ 64 ] presented an aerial IRT defect inspection procedure for PV power plants utilizing a two-step autonomous flight strategy to decrease the inspection duration (approximately 60 %) and associated costs. The first step involved a fast-high-altitude flight for detecting possible defect location.…”