Impact of measurement selection on load model parameter estimation

Siming Guo, Komal S. Shetye, Thomas J. Overbye, Hao Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Measurement-based load model parameter estimation uses measurements from a disturbance on the grid. Those measurements can include voltage and/or real and reactive power. In this paper, we show that the type of measurements used directly impacts the accuracy of parameter estimation. We look at four scenarios. With wide-area deployment of voltage sensors, such as PMUs, the resulting parameter estimation is very accurate at high signal-to-noise ratios (SNR), but is very poor at low SNRs, because voltage has low sensitivity to the parameters. With only local deployment of complex power sensors, the estimate is worse than the first scenario at all SNRs. However, with wide-area deployment of complex power sensors, the estimate becomes very robust to low SNR, because complex power has much higher sensitivity to the parameters. Combining wide-area voltage and power measurements produces the best results.

Original languageEnglish (US)
Title of host publication2017 IEEE Power and Energy Conference at Illinois, PECI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509055517
DOIs
StatePublished - May 30 2017
Event2017 IEEE Power and Energy Conference at Illinois, PECI 2017 - Urbana, United States
Duration: Feb 23 2017Feb 24 2017

Publication series

Name2017 IEEE Power and Energy Conference at Illinois, PECI 2017

Other

Other2017 IEEE Power and Energy Conference at Illinois, PECI 2017
CountryUnited States
CityUrbana
Period2/23/172/24/17

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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    Guo, S., Shetye, K. S., Overbye, T. J., & Zhu, H. (2017). Impact of measurement selection on load model parameter estimation. In 2017 IEEE Power and Energy Conference at Illinois, PECI 2017 [7935736] (2017 IEEE Power and Energy Conference at Illinois, PECI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PECI.2017.7935736