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  • Research Article
  • Open Access

Challenges and Trends in Analyses of Electric Power Quality Measurement Data

EURASIP Journal on Advances in Signal Processing20072007:057985

  • Received: 13 August 2006
  • Accepted: 13 November 2006
  • Published:


Power quality monitoring has expanded from a means to investigate customer complaints to an integral part of power system performance assessments. Besides special purpose power quality monitors, power quality data are collected from many other monitoring devices on the system (intelligent relays, revenue meters, digital fault recorders, etc.). The result is a tremendous volume of measurement data that is being collected continuously and must be analyzed to determine if there are important conclusions that can be drawn from the data. It is a significant challenge due to the wide range of characteristics involved, ranging from very slow variations in the steady state voltage to microsecond transients and high frequency distortion. This paper describes some of the problems that can be evaluated with both offline and online analyses of power quality measurement data. These applications can dramatically increase the value of power quality monitoring systems and provide the basis for ongoing research into new analysis and characterization methods and signal processing techniques.


  • Slow Variation
  • Power Quality
  • Signal Processing Technique
  • State Voltage
  • Customer Complaint

Authors’ Affiliations

Electric Power Research Institute (EPRI Solutions), Knoxville, TN 37932, USA
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712-0240, USA


  1. Dugan RC, McGranaghan MF, Santoso S, Beaty HW: Electrical Power Systems Quality, McGraw-Hill Professional Engineering Series. 2nd edition. McGraw-Hill, New York, NY, USA; 2003.Google Scholar
  2. Santoso S, Lamoree J, Bingham R: Answermodule: autonomous expert systems for turning raw PQ measurements into answers. Proceedings of 9th International Conference on Harmonics and Quality of Power, October 2000, Orlando, Fla, USA 499–503.Google Scholar
  3. Fayyad UM, Piatetsky-Shapiro G, Smyth P: From data mining to knowledge discovery: an overview. In Advances in Knowledge Discovery and Data Mining. Edited by: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. MIT Press, Cambridge, Mass, USA; 1996:1-34.Google Scholar
  4. Melhorn CJ, McGranaghan MF: Interpretation and analysis of power quality measurements. IEEE Transactions on Industry Applications 1995,31(6):1363-1370. 10.1109/28.475727View ArticleGoogle Scholar
  5. Djokić SŽ, Milanović JV, Chapman DJ, McGranaghan MF: Shortfalls of existing methods for classification and presentation of voltage reduction events. IEEE Transactions on Power Delivery 2005,20(2, part 2):1640-1649. 10.1109/TPWRD.2004.833880View ArticleGoogle Scholar
  6. Djokić SŽ, Milanović JV, Chapman DJ, McGranaghan MF, Kirschen DS: A new method for classification and presentation of voltage reduction events. IEEE Transactions on Power Delivery 2005,20(4):2576-2584. 10.1109/TPWRD.2005.852322View ArticleGoogle Scholar
  7. Brooks DL, Sabin DD: An assessment of distribution system power quality: volume 3: the library of distribution system power quality monitoring case studies. In Tech. Rep. 106294. Electric Power Research Institute, Palo Alto, Calif, USA; 1996.Google Scholar
  8. Santoso S, Dugan RC, Lamoree J, Sundaram A: Distance estimation technique for single line-to-ground faults in aradial distribution system. IEEE of Power Engineering Society Winter Meeting, January 2000, Singapore 4: 2551–2555.Google Scholar


© M. F. McGranaghan and S. Santoso. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.