Abstract
Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient.
Highlights
With the growing demand for a low-consumption economy and thanks to technological advances, photovoltaic (PV) energy generation has become paramount in the production of renewable energy.Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution.For this reason, PV power plants are one of the main systems adopted to produce clean energy.Huge investments have been allocated by European countries to stimulate the use of so-called clean energy
SolAIr, an artificial intelligence unmanned aerial vehicles (UAV)-based inspection system was presented, which is capable of detecting faults in large-scale PV plants
The proposed approach for defect analysis can be an essential aid to assist operators for Operation and Maintenance (O&M) operations, reducing cost and errors arising from manual operations
Summary
With the growing demand for a low-consumption economy and thanks to technological advances, photovoltaic (PV) energy generation has become paramount in the production of renewable energy.Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution.For this reason, PV power plants are one of the main systems adopted to produce clean energy.Huge investments have been allocated by European countries to stimulate the use of so-called clean energy. With the growing demand for a low-consumption economy and thanks to technological advances, photovoltaic (PV) energy generation has become paramount in the production of renewable energy. Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, PV power plants are one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is crucial; detecting the degradation of solar panels is the only way to ensure good performance over time. Besides avoiding a waste of energy, the reason for maintaining a correct functional status of a plant is economic: the degradation of long-term performance and overall reliability of PV plants can drastically reduce expected revenues [1,2]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.