In the last two decades, the increased production and installation of photovoltaic (PV) plants worldwide has asked for efficient low-cost methods for PV plant inspection to monitor their functionality and guaranteed their performance. To lower maintenance costs new systems have been thought to substitute human workers inspecting the PV plants. The employment of Unmanned Aerial Vehicles (UAVs) has allowed realizing a fast detection of defects and problems arisen in PV plants thanks to the fusion of computer vision algorithms and high accuracy Global Navigation Satellite System (GNSS) positioning techniques able to detect and tag anomalies and identify the defective panels. Authors in this paper intend to present the state-of-the-art in the Computer Vision field applied to PV plant inspection and to thermal anomalies detection over the panels. In addition, different data sets have been recorded and compared for geo-referencing the solar panels. They have been derived through the U-blox NEO-M8N installed on board of the UAV used for inspection. Although the U-blox NEO-M8N measures are less accurate than the classic RTK GNSS ones, the measurements obtained with this handset introduce a very interesting novelty since initial services of the Galileo constellation, supported by the NEO-M8N GNSS module, have become available only since last December. Future testing and validation will be performed by using geo-referenced data from the RTK GNSS receiver, that has been ordered with a specially customized antenna whose specifications have been properly designed and sent to the manufacturer for its fabrication. Next campaigns will allow to get results also from this RTK receiver and to properly validate the proposed algorithm, by comparing new results with those found through the employment of U-blox receiver.

A UAV infrared measurement approach for defect detection in photovoltaic plants

Angrisano A.;
2017

Abstract

In the last two decades, the increased production and installation of photovoltaic (PV) plants worldwide has asked for efficient low-cost methods for PV plant inspection to monitor their functionality and guaranteed their performance. To lower maintenance costs new systems have been thought to substitute human workers inspecting the PV plants. The employment of Unmanned Aerial Vehicles (UAVs) has allowed realizing a fast detection of defects and problems arisen in PV plants thanks to the fusion of computer vision algorithms and high accuracy Global Navigation Satellite System (GNSS) positioning techniques able to detect and tag anomalies and identify the defective panels. Authors in this paper intend to present the state-of-the-art in the Computer Vision field applied to PV plant inspection and to thermal anomalies detection over the panels. In addition, different data sets have been recorded and compared for geo-referencing the solar panels. They have been derived through the U-blox NEO-M8N installed on board of the UAV used for inspection. Although the U-blox NEO-M8N measures are less accurate than the classic RTK GNSS ones, the measurements obtained with this handset introduce a very interesting novelty since initial services of the Galileo constellation, supported by the NEO-M8N GNSS module, have become available only since last December. Future testing and validation will be performed by using geo-referenced data from the RTK GNSS receiver, that has been ordered with a specially customized antenna whose specifications have been properly designed and sent to the manufacturer for its fabrication. Next campaigns will allow to get results also from this RTK receiver and to properly validate the proposed algorithm, by comparing new results with those found through the employment of U-blox receiver.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/3205470
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