The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

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.

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Historical villages represent a highly vulnerable cultural heritage; their preservation can be ensured thanks to technological innovations in the field of geomatics and information systems. Among these, Geographical Information Systems (GISs) allow exploiting heterogeneous data for efficient vulnerability assessment, in terms of both time and usability. Geometric attributes, which currently are mainly inferred by visual inspections, can be extrapolated from data obtained by geomatic technologies. Furthermore, the integration with non-metric data ensures a more complete description of the post-seismic risk thematic mapping. In this paper, a high-performance information system for small urban realities, such as historical villages, is described, starting from the 3D survey obtained through the integrated management of recent innovative geomatic sensors, such as Unmanned Aerial Vehicles (UAVs), Terrestrial Laser Scanners (TLSs), and 360º images. The results show that the proposed strategy of the automatic extraction of the parameters from the GIS can be generalized to other case studies, thus representing a straightforward method to enhance the decision-making of public administrations. Moreover, this work confirms the importance of managing heterogeneous geospatial data to speed up the vulnerability assessment process. The final result, in fact, is an information system that can be used for every village where data have been acquired in a similar way. This information could be used in the field by means of a GIS app that allows updating the geospatial database, improving the work of technicians. This approach was validated in Gabbiano(Pieve Torina), a village in Central Italy affected by earthquakes in 2016 and 2017.

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Models to account for Fowler-Nordheim tunneling and hot electron injection through silicon dioxide as well as bandto-band tunneling in silicon have been incorporated into the 2-D device simulator HFJELDS(1J. These represent all the important physical mechanism which occur during the writing of flmh EEPROM devices. The physical models have been verified by comparison with flash EEPROM devices fabricated using a 1.0 pm technology where good agreement has been obtained.

The aim of this manuscript is to define an operational pipeline of work, from data acquisition to the report creation, for the smart management of PV plants. To achieve such an ambitious result, we exploit the implementation of a conceptual model, deployed through a relational database to retrieve any kind of information related to the PV plant. The motivation that drove this research is due to the increasing construction of PV plants. In fact, following European and international investments that heavily stimulated the use of clean energy, the need to maintain PV plants in their maximum efficiency for their whole lifecycle emerged, to bring about benefits from both the ecological and the economic points of view. While the research community focuses on finding new and automatic ways to detect faults automatically, few efforts have been made considering the so-called Operation and Maintenance (O&M). A relational conceptual model may facilitate the management of heterogeneous sources of information, which are common in complex PV plants. The purpose of the present study is to provide companies and insiders with a GIS-based tool to maintain the energy efficiency of a PV plant. Indeed, it is a common practice used by companies dealing with O&M of PV plants to create technical reports about the health status of the plants. This operation, made manually, is very time consuming and error prone. To overcome this latter drawback, this work attempts to encourage the use of GIS in the PV plants O&M, which proves to be efficient to deal with fault management and to assure a good level of energy production. The developed conceptual model, tested on two real case studies, proved to be complete, cost-effective and efficient to be replicated in other existing plants.