NDLI: Data mining framework for power quality event characterization of iron and steel plants

Content Provider	IEEE Xplore Digital Library
Author	Guder, M. Salor, O. Cadirci, I. Ozkan, B. Altintas, E.
Copyright Year	2014
Description	Author affiliation: Electr. & Electron. Eng. Dept., Hacettepe Univ., Ankara, Turkey (Cadirci, I.) \|\| Electr. & Electron. Eng. Dept., Gazi Univ., Ankara, Turkey (Salor, O.; Altintas, E.) \|\| Comput. Eng. Dept., Middle East Tech. Univ., Ankara, Turkey (Guder, M.; Ozkan, B.)
Abstract	In this paper, a power quality (PQ) knowledge discovery and modeling framework has been developed for both temporal and spatial PQ event data collected from transformer substations supplying iron and steel (I&S) plants. PQ event characteristics of various I&S plants have been obtained based on clustering and rule discovery techniques. The data are collected by the PQ analyzers, which detect the voltage sags, swells, and interruptions according to the IEC Standard 61000-4-30. The constructed clustering strategy ensures feasible system monitoring by reducing unmanageable number of PQ events collected by the distributed PQ measurement systems into event clusters count. An abstraction for event representation has been developed, through which representative feature bags are constructed for each event to be used in the similarity decisions. The developed model has been applied satisfactorily to PQ event data obtained from 15 major transformer substations supplying heavy industry zones of the transmission system up to a five-year time period and from two additional transformer substations supplying some other industrial zones, for comparison purposes. The developed PQ data mining framework, which is used to identify PQ event distributions based on the event descriptions given in the IEEE Std. 1159, provides a useful analysis and evaluation infrastructure for taking countermeasures against the most probable event occurrences, specific to those feeders of I&S plant transformer substations.
Starting Page	1
Ending Page	11
File Size	1371092
Page Count	11
File Format	PDF
ISBN	9781479922888
DOI	10.1109/IAS.2014.6978449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-05
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Voltage fluctuations Substations Power quality Pattern clustering Power system reliability Metals industry Data mining Metal industry Power system faults Monitoring Power quality (PQ)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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