Clustering of power quality event data collected via monitoring systems installed on the electricity network

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Clustering of power quality event data collected via monitoring systems installed on the electricity network

Full text

Pdf (322 KB)

Source

International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data table of contents

Paris, France

SESSION: Short research papers table of contents

Pages 124-130

Year of Publication: 2009

ISBN:978-1-60558-668-7

Authors

Mennan Güder	Hacettepe University, Ankara, Turkey and Middle East Technical University, Ankara, Turkey
Nihan Kesim Çiçekli	Middle East Technical University, Ankara, Turkey
Özgül Salor	Hacettepe University, Ankara, Turkey
Işik Çadirci	Hacettepe University, Ankara, Turkey

Sponsors

: Cooperating Objects Network of Excellence (CONET)
: Geographic Information Science and Technology (GIST) Group at Oak Ridge National Laboratory
: Computational Sciences and Engineering (CSE) Division at the Oak Ridge National Laboratory

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 10, Citation Count: 0

Additional Information:

abstract references index terms

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1601966.1601986 What is a DOI?

ABSTRACT

In this paper, a k-means-based clustering method applied to power quality event data is described. The data are collected by the power quality (PQ) monitors, which are developed through the National PQ Project and installed on the electricity network. The PQ monitors detect the PQ events defined as voltage sags, swells, and interruptions by the IEC Standard 61000-4-30, and collect the raw data of the event. The proposed method aims to cope with the huge event data size and cluster the event types so that PQ events are ultimately classified. The method helps to manage the event data to come up with PQ assessments for the specific measurement points and to make comparisons of various measurement points in terms of PQ of the electricity network.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	A. Asheibi, D. Stirling, D. Soetanto, "Analyzing Harmonic Monitoring Data Using Data Mining", 5^th Australian Data Mining Conference, AusDM, 2006.
	2	P. K. Dash, I. L. W. Chun, M. V. Chilukuri, "Power Quality Data Mining Using Soft Computing and Wavelet Transform", <u>Conference on Convergent Technologies for Asia-Pacific Region</u>, TENCON 2003.
	3	A. Asheibi, D. Stirling, D. Robinson, "Identification of Load Power Quality Characteristics Using Data Mining", IEEE CCECE/CCGEI, 2006.
	4	O. N. Gerek, D. G. Ece, A. Barkana, "Covariance Analysis of Voltage Waveform Signature for Power-Quality Event Classification", IEEE Trans. on Power Delivery, vol. 21, no. 4, Oct. 2006.
	5	M. Wang, G. I. Rowe, A. V. Mamishev, "Classification of Power Quality Events Using Optimal Time-Frequency Representations --- Part 2: Application", IEEE Trans. on Power Delivery, vol. 19, no. 3, July 2003.
	6	M. Uyar, S. Yildirim, M. T. Gencoglu, "An expert system based on S-transform and neural network for automatic classification of power quality disturbances", Expert Systems with Applications, Elsevier, 2008.
	7	Y. Liao, J. B. Lee, "A fuzzy expert system for classifying power quality disturbances", Electrical Power and Energy Systems, vol. 26, pp. 199--205, 2004.
	8	G. S. Hu, F. F. Zhu, Z. Ren, "Power quality disturbance identification using wavelet packet entropy and weighted support vector machines", Expert Systems with Applications, vol. 35, pp. 143--149, 2008.