Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants

Youn

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2017

ABSTRACT:As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Section: Introductionmentioning

confidence: 99%

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Youn

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2017

Journal of Advanced Transportation

“…In this case, monitoring large areas of land or sea is the main purpose. It concerns such areas of human activity as an agriculture [4,5], energetics (i.e., photovoltaic plants [6][7][8] and high voltage lines [9]), environment protection [10], search and rescue [11], forestry and fire detection [12,13], water area management [14,15], and so on. An automation of monitoring procedures, low costs, and minimization of human resources in the UAV exploitation are conducive to the dynamic growth of their use in the civilian applications.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method requires the use of a broadband control channel and may only be used during daylight and in good visibility conditions. Under night conditions and in poor visibility, a thermal imaging camera [6,[23][24][25] or synthetic aperture radar (SAR) [26][27][28] may be used. Furthermore, long distance wireless communications are characterized by significant delays and, in the case of the hard-to-reach areas, only satellite communications [29] can be used.…”

Section: Introductionmentioning

confidence: 99%

Doppler Effect-Based Automatic Landing Procedure for UAV in Difficult Access Environments

Kelner

Ziółkowski

2017

Currently, almost unrestricted access to low-lying areas of airspace creates an opportunity to use unmanned aerial vehicles (UAVs), especially those capable of vertical take-off and landing (VTOL), in transport services. UAVs become increasingly popular for transporting postal items over small, medium, and large distances. It is forecasted that, in the near future, VTOL UAVs with a high take-off weight will also deliver goods to very distant and hard-to-reach locations. Therefore, UAV navigation plays a very important role in the process of carrying out transport services. At present, during the flight phase, drones make use of the integrated global navigation satellite system (GNSS) and the inertial navigation system (INS). However, the inaccuracy of GNSS + INS makes it unsuitable for landing and take-off, necessitating the guidance of a human UAV operator during those phases. Available navigation systems do not provide sufficiently high positioning accuracy for an UAV. For this reason, full automation of the landing approach is not possible. This paper puts forward a proposal to solve this problem. The authors show the structure of an autonomous system and a Doppler-based navigation procedure that allows for automatic landing approaches. An accuracy evaluation of the developed solution for VTOL is made on the basis of simulation studies.

Journal of the Korean Society of Surveying, Geodesy, Photogramm

“…Visual inspection and output measurement methods can be used for fault diagnosis in PV panels with reduced output efficiency (Quater et al, 2014). However, given that many large-scale PV power plants will be constructed and operated in the future within the climate change response framework, large-scale PV power plant monitoring through visual inspection will be uneconomical in terms of time and expenses.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Malfunctioning Photovoltaic Modules Using Unmanned Aerial Vehicle Thermal Infrared Images

Kim¹,

Youn²,

Kim³

2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.