13 research outputs found

    Calibration of voltage and current transducers for dc railway systems

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    To establish a single European railway area, the European Commission requires, by 2019, that energy billings shall be computed on the actual energy consumed. So, in the near future, all the trains shall be equipped with an energy measurement system, whose measurement accuracy should be assessed and periodically reverified, as required by EN 50463-2. As for every energy and power measuring system, the voltage and current transducers play a crucial role as their accuracy could determine the performance level of the entire measurement chain. To answer to this emerging need, this paper presents a calibration system allowing the accurate testing of dc voltage and current transducers, up to 6 kV and 300 A and up to 10 kHz. It is able to reproduce all the tests prescribed by EN 50463-2, but in order to characterize the transducers in actual operating conditions, a series of additional tests can also be performed using synthetic complex waveforms or even signals acquired on-board trains. The expanded uncertainty (level of confidence 95%) of the calibration system is 43 mu extV /V and 24 mu extA /A at dc and 520 mu extV /V and 820 mu extA /A at 10 kHz. Moreover, the calibration of two commercial voltage and current transducers, currently installed in the trains of an Italian operator, is presented

    On the importance of characterizing virtual pmus for hardware‐in‐the‐loop and digital twin applications

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    open5noThis research was funded by EdgeFLEX, grant number 883710. This project received funding from the European Union’s Horizon 2020 research and innovation program.In recent years, the introduction of real‐time 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 hardware‐in‐the‐loop (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 real‐time 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

    Metrological characterization of sensors and instrumentation for distribution grid monitoring and electrical asset diagnostics

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    The Smart Grid needs a large amount of information to be operated and day by day new information is required to improve the operation performance. It is also fundamental that the available information is reliable and accurate. Therefore, the role of metrology is crucial, especially if applied to the distribution grid monitoring and the electrical assets diagnostics. This dissertation aims at better understanding the sensors and the instrumentation employed by the power system operators in the above-mentioned applications and studying new solutions. Concerning the research on the measurement applied to the electrical asset diagnostics: an innovative drone-based measurement system is proposed for monitoring medium voltage surge arresters. This system is described, and its metrological characterization is presented. On the other hand, the research regarding the measurements applied to the grid monitoring consists of three parts. The first part concerns the metrological characterization of the electronic energy meters’ operation under off-nominal power conditions. Original test procedures have been designed for both frequency and harmonic distortion as influence quantities, aiming at defining realistic scenarios. The second part deals with medium voltage inductive current transformers. An in-depth investigation on their accuracy behavior in presence of harmonic distortion is carried out by applying realistic current waveforms. The accuracy has been evaluated by means of the composite error index and its approximated version. Based on the same test setup, a closed-form expression for the measured current total harmonic distortion uncertainty estimation has been experimentally validated. The metrological characterization of a virtual phasor measurement unit is the subject of the third and last part: first, a calibrator has been designed and the uncertainty associated with its steady-state reference phasor has been evaluated; then this calibrator acted as a reference, and it has been used to characterize the phasor measurement unit implemented within a real-time simulator

    Machine Learning and Data Mining Applications in Power Systems

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    This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

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    Online learning of physics during a pandemic: A report from an academic experience in Italy

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    The arrival of the Sars-Cov II has opened a new window on teaching physics in academia. Frontal lectures have left space for online teaching, teachers have been faced with a new way of spreading knowledge, adapting contents and modalities of their courses. Students have faced up with a new way of learning physics, which relies on free access to materials and their informatics knowledge. We decided to investigate how online didactics has influenced students’ assessments, motivation, and satisfaction in learning physics during the pandemic in 2020. The research has involved bachelor (n = 53) and master (n = 27) students of the Physics Department at the University of Cagliari (N = 80, 47 male; 33 female). The MANOVA supported significant mean differences about gender and university level with higher values for girls and master students in almost all variables investigated. The path analysis showed that student-student, student-teacher interaction, and the organization of the courses significantly influenced satisfaction and motivation in learning physics. The results of this study can be used to improve the standards of teaching in physics at the University of Cagliar

    A low-voltage measurement testbed for metrological characterization of algorithms for phasor measurement units

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    In the recent years, the scientific community is paying great attention to the development of phasor measurement units (PMU) and, particularly, to novel digital signal processing algorithms for these instruments. While the performance of estimation algorithms is usually evaluated through simulations on a PC, the overall PMU accuracy depends on measurement hardware as well (e.g., transducers, data converters, and synchronization circuitry). However, the relationship between estimation algorithms accuracy and metrological characteristics of hardware equipment is very difficult to predict or to simulate. Therefore, the performance of different algorithms can be hardly compared when they are actually implemented in real instruments and used in the field. This paper attempts to address this problem by presenting an open testbed for PMU estimation algorithms. The key distinctive feature of the testbed is its ability to evaluate and to compare algorithm accuracy under experimental conditions that include not only the disturbances specified in the IEEE Standard C37.118.1-2011 and its Amendment IEEE C37.118.1a-2014, but also the effects of given uncertainty contributions due to different hardware components. A thorough metrological characterization of the testbed, properly supported by a noise propagation model, is performed in order to quantify such uncertainty contributions and their impact on the estimates of synchrophasor magnitude, phase, fundamental frequency, and rate of change of frequency returned by different algorithms under test. As a case study, three state-of-the-art estimation algorithms are tested in a variety of conditions
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