13,079 research outputs found
Integration of conventional and unconventional Instrument Transformers in Smart Grids
In this thesis the reader will be guided towards the role of Instrument Transformers inside the always evolving Smart Grid scenario. In particular, even non-experts or non-metrologists will have the chance to follow the main concepts presented; this, because the basic principles are always presented before moving to in-deep discussions.
The chapter including the results of the work is preceded by three introductive chapters. These, contain the basic principles and the state of the art necessary to provide the reader the tools to approach the results chapter.
The first three chapters describe: Instrument Transformers, Standards, and Metrology. In the first chapter, the studied Instrument Transformers are described and compared with particular attention to their accuracy parameters. In the second chapter instead, two fundamental international documents, concerning Instrument Transformers, are analysed: the IEC 61869 series and the EN 50160. This has been done to be completely aware of how transformers are standardized and regulated. Finally, the last introductive chapter presents one of the pillars of this work: metrology and the role of uncertainty.
In the core of the work Instrument Transformers integration in Smart Grid is distinguished in two main topics. The first assesses the transformers behaviour, in terms of accuracy, when their normal operation is affected by external quantities. The second exploits the current and voltage measurements obtained from the transformers to develop new algorithm and techniques to face typical and new issue affecting Smart Grids.
In the overall, this thesis has a bifold aim. On one hand it provides a quite-detailed overview on Instrument Transformers technology and state of the art. On the other hand, it describes issues and novelties concerning the use of the transformers among Smart Grids, focusing on the role of uncertainty when their measurements are used for common and critical applications
Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors
Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe
On the importance of characterizing virtual pmus for hardwareâinâtheâloop and digital twin applications
open5noThis research was funded by EdgeFLEX, grant number 883710. This project received
funding from the European Unionâs Horizon 2020 research and innovation program.In recent years, the introduction of realâtime simulators (RTS) has changed the way of researching the power network. In particular, researchers and system operators (SOs) are now ca-pable of simulating the complete network and of making it interact with the real world thanks to the hardwareâinâtheâloop (HIL) and digital twin (DT) concepts. Such tools create infinite scenarios in which the network can be tested and virtually monitored to, for example, predict and avoid faults or energy shortages. Furthermore, the realâtime monitoring of the network allows estimating the status of the electrical assets and consequently undertake their predictive maintenance. The success of the HIL and DT application relies on the fact that the simulated network elements (cables, gener-ation, accessories, converters, etc.) are correctly modeled and characterized. This is particularly true if the RTS acquisition capabilities are used to enable the HIL and the DT. To this purpose, this work aims at emphasizing the role of a preliminary characterization of the virtual elements inside the RTS system, experimentally verifying how the overall performance is significantly affected by them. To this purpose, a virtual phasor measurement unit (PMU) is tested and characterized to understand its uncertainty contribution. To achieve that, firstly, the characterization of a virtual PMU calibrator is described. Afterward, the virtual PMU calibration is performed, and the results clearly highlight its key role in the overall uncertainty. It is then possible to conclude that the characterization of the virtual elements, or models, inside RTS systems (omitted most of the time) is fundamental to avoid wrong results. The same concepts can be extended to all those fields that exploit HIL and DT capa-bilities.openMingotti A.; Costa F.; Cavaliere D.; Peretto L.; Tinarelli R.Mingotti A.; Costa F.; Cavaliere D.; Peretto L.; Tinarelli R
Complex networks and data mining: toward a new perspective for the understanding of complex systems
Complex systems, i.e. systems composed of a large set of elements interacting in a
non-linear way, are constantly found all around us. In the last decades, different approaches
have been proposed toward their understanding, one of the most interesting
being the Complex Network perspective. This legacy of the 18th century mathematical
concepts proposed by Leonhard Euler is still current, and more and more relevant in
real-world problems. In recent years, it has been demonstrated that network-based representations
can yield relevant knowledge about complex systems. In spite of that, several
problems have been detected, mainly related to the degree of subjectivity involved
in the creation and evaluation of such network structures. In this Thesis, we propose addressing
these problems by means of different data mining techniques, thus obtaining a
novel hybrid approximation intermingling complex networks and data mining. Results
indicate that such techniques can be effectively used to i) enable the creation of novel network
representations, ii) reduce the dimensionality of analyzed systems by pre-selecting
the most important elements, iii) describe complex networks, and iv) assist in the analysis
of different network topologies. The soundness of such approach is validated through
different validation cases drawn from actual biomedical problems, e.g. the diagnosis of
cancer from tissue analysis, or the study of the dynamics of the brain under different
neurological disorders
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