Bonavolontà, Francesco (2015) A MEASUREMENT ARCHITECTURE BASED ON COMPRESSIVE SAMPLING FOR THE MONITORING OF ELECTRICAL POWER TRANSMISSION GRIDS. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: A MEASUREMENT ARCHITECTURE BASED ON COMPRESSIVE SAMPLING FOR THE MONITORING OF ELECTRICAL POWER TRANSMISSION GRIDS
Creators:
CreatorsEmail
Bonavolontà, Francescofrancesco.bonavolonta@unina.it
Date: March 2015
Number of Pages: 110
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Scuola di dottorato: Ingegneria industriale
Dottorato: Ingegneria elettrica
Ciclo di dottorato: 27
Coordinatore del Corso di dottorato:
nomeemail
Serpico, Claudioserpico@unina.it
Tutor:
nomeemail
D'Arco, MauroUNSPECIFIED
Schiano Lo Moriello, RosarioUNSPECIFIED
Date: March 2015
Number of Pages: 110
Uncontrolled Keywords: Compressive Sampling, Power Quality
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/07 - Misure elettriche e elettroniche
Date Deposited: 13 Apr 2015 10:07
Last Modified: 24 Sep 2015 10:25
URI: http://www.fedoa.unina.it/id/eprint/10177
DOI: 10.6092/UNINA/FEDOA/10177

Abstract

In recent years significant change in the power grid for the distribution of electrical energy have been observed. Thanks to liberalization of the electrical market and the incentives offered to the production of energy from renewable sources, the number of medium and small producers has increased. Moreover, with the development of communication technology, the transmission grid has been increasingly equipped with automated devices capable of monitoring and transmitting some information about the grid and, in some cases, to control the actuation devices connected to the grid itself. In this scenario, the term smart grid was born, used to describe energy networks that can automatically monitor energy flows and adjust to changes in energy supply and demand accordingly. When coupled with smart metering systems, smart grids reach consumers and suppliers by providing information on real-time consumption. As an example, consumers can adapt their energy usage to different energy prices throughout the day with personal smart meters, saving money on their energy bills by consuming more energy in lower price periods. As it can be expected, the wider the grid, the larger the number of smart meters required for its monitoring. However, deploying thousands of such devices turns rapidly to be too expensive. Consequently, the realization of the distributed measurement system could not be economically sustainable. To overcome the considered limitations, the research activities have been focused on the possibility to move towards a different approach for monitoring grid deployed on wide geographical areas, exploiting the advantages of an innovative acquisition paradigm: the Compressed Sampling (CS) . CS is a signal processing technique for efficiently acquiring and reconstructing a signal from far fewer samples than those required by the Shannon-Nyquist sampling theorem. In particular, the proposed architecture consists of low cost nodes mandated only to sample and digitize a limited number of input signal samples and transmit them to a central measurement unit, thus saving the costs related to large memory supports and expensive digital processing units. Once the samples are received, the central unit recovers the signal spectrum thanks to CS-based algorithm and carries out the desired measurements. Thanks to the CS-based approach, it is possible to design and realize a measurement node, characterized by reduced memory depth and only one ADC, suitable for poly-phase system with neutral wire that allows meeting the requirements of a distributed measurement system. Numerical and experimental test verified highlight the capability of CS-based acquisition approach of correctly measuring the root-mean-square amplitude of voltage waveforms and assuring simultaneous multi-channel acquisitions. Finally, the compliance of the measurement node based on CS approach with the current Power Quality standards is assessed and discussed.

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