A Sensitivity Based Approach for Efficient PMU Deployment on Smart Grid

  • Richard Barella
  • Duc Nguyen
  • Ryan Winter
  • Kuei-Ti Lu
  • Scott Wallace
  • Xinghui Zhao
  • Eduardo Cotilla-Sanchez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 579)

Abstract

Smart grid technology utilizes phasor measurement units (PMUs) as the key devices to provide synchronized measurements on an electrical grid, enabling wide area monitoring and control. Due to the high cost of deploying and maintaining these devices, an efficient placement strategy is essential in enhancing the reliability of a power grid at a relatively low cost. In this paper, we propose a novel PMU deployment method based on the effectiveness of detecting line faults. We have carried out a sensitivity study of a PMU-based fault detection method using three different distance metrics and used the study as a guideline for efficient PMU deployment. To illustrate the effectiveness of this approach, we have derived a number of alternative PMU placement plans for a power grid from a protection perspective. Experimental results show that many of our PMU placement plans greatly reduce the required PMU deployment (up to 80 %) as compared to the original placement, yet still provides similar level of accuracy in fault detection.

Keywords

Smart grid PMU deployment Sensitivity Fault detection Accuracy 

Notes

Acknowledgement

The generous support from Bonneville Power Administration and Oregon BEST through the NW Energy XP Award is gratefully acknowledged. The authors also would like to thank Bonneville Power Administration for providing PMU data used in this research.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Richard Barella
    • 1
  • Duc Nguyen
    • 1
  • Ryan Winter
    • 1
  • Kuei-Ti Lu
    • 1
  • Scott Wallace
    • 1
  • Xinghui Zhao
    • 1
  • Eduardo Cotilla-Sanchez
    • 2
  1. 1.School of Engineering and Computer ScienceWashington State UniversityVancouverUSA
  2. 2.School of Electrical Engineering and Computer ScienceOregon State UniversityCorvallisUSA

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