Nowadays the photovoltaic business is focusing its attention on asset management of large PV plants. These activities can take advantage of the employment of Unmanned Aerial Vehicles (UAV) able to provide a large number of useful data for finding a vast range of potential issues able to compromise the performance and the energy yield of the plant itself. Due to the large amount of data available, it is important to have an effective method to process them with a reduced or null human supervision. The aim of this paper is to show an improved mosaicking technique used in an advanced semiautomatic method for the analysis of data captured by Unmanned Aerial Vehicles (UAV) to improve the effectiveness of PV plant monitoring while reducing the required time. The improved complete method is assessed using IR and Visual images captured by UAVs; these are analyzed and, eventually, is automatically produced a final digital map of the PV plant in which the potential defects are highlighted.

A semi-automated method for Defect Identification in large Photovoltaic power plants using Unmanned Aerial Vehicles

Francesco Grimaccia;Sonia Leva;Alessandro Niccolai
2018-01-01

Abstract

Nowadays the photovoltaic business is focusing its attention on asset management of large PV plants. These activities can take advantage of the employment of Unmanned Aerial Vehicles (UAV) able to provide a large number of useful data for finding a vast range of potential issues able to compromise the performance and the energy yield of the plant itself. Due to the large amount of data available, it is important to have an effective method to process them with a reduced or null human supervision. The aim of this paper is to show an improved mosaicking technique used in an advanced semiautomatic method for the analysis of data captured by Unmanned Aerial Vehicles (UAV) to improve the effectiveness of PV plant monitoring while reducing the required time. The improved complete method is assessed using IR and Visual images captured by UAVs; these are analyzed and, eventually, is automatically produced a final digital map of the PV plant in which the potential defects are highlighted.
2018 IEEE Power & Energy Society General Meeting (PESGM)
PV plant monitoring; Unmanned Aerial Vehicles; Image processing; PV defect identification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1084140
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