Using wavelet synopsis techniques on electric power system measurements

Moutis, P; Hatziargyriou, ND

dc.contributor.author	Moutis, P	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T02:53:31Z
dc.date.available	2014-03-01T02:53:31Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36380
dc.subject	Garofalakis-Kumar	en
dc.subject	Haar transformation	en
dc.subject	Power system measurements	en
dc.subject	Wavelet synopsis	en
dc.subject.other	Compress time	en
dc.subject.other	Electric power	en
dc.subject.other	Exponential increase	en
dc.subject.other	Garofalakis-Kumar	en
dc.subject.other	Haar transformation	en
dc.subject.other	Historical data	en
dc.subject.other	Interconnected grid	en
dc.subject.other	Power system measurement	en
dc.subject.other	Sampling time	en
dc.subject.other	Small island	en
dc.subject.other	Transmission system operators	en
dc.subject.other	Wavelet synopsis	en
dc.subject.other	Data compression	en
dc.subject.other	Exhibitions	en
dc.subject.other	Rating	en
dc.subject.other	Smart power grids	en
dc.subject.other	Time series	en
dc.subject.other	Quality control	en
dc.title	Using wavelet synopsis techniques on electric power system measurements	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ISGTEurope.2011.6162680	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ISGTEurope.2011.6162680	en
heal.identifier.secondary	6162680	en
heal.publicationDate	2011	en
heal.abstract	The elaboration of power system data is of crucial importance to the study of power system quality, control and development. Considering the widely interconnected grid and its future expansions, the integration of historical data (namely time series of various scales) to databases, implies the exponential increase of their size. Even in the case of small island systems, the logging of numerous values in sampling time of seconds can lead to similar results. Moreover, since some central assessment by Transmission System Operators (TSOs) has to be executed, the need to transmit this information over a network of given capacity also rises. To face the above issues, methods to compress time series data are examined in this paper. The wavelet synopsis techniques of Garofalakis-Kumar and the Greedy are applied for queries evaluated according to the L2 metric, while the Garofalakis-Kumar and the Selection of the Top-k Haar coefficients are used for queries evaluated according to the L metric. The reconstructed time series, after the application of each synopsis technique, is compared to the original one according to specific criteria. Suggestions for further study and research are pointed out. © 2011 IEEE.	en
heal.journalName	IEEE PES Innovative Smart Grid Technologies Conference Europe	en
dc.identifier.doi	10.1109/ISGTEurope.2011.6162680	en