Steady-state inertia estimation using a neural network approach with modal information

Andreas Schmitt, Byong Jun Lee

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

2 Citations (Scopus)

Abstract

The inertia of a power grid plays a significant role in maintaining the stability of a system. If the inertia is large enough, stable operating conditions can be maintained during small scale events. As the percentage of power supplied by renewable energy sources increases, the value of inertia in a system will decrease. Therefore, it has become necessary to accurately estimate the inertia in the system. Traditional methods of estimating the inertia make use of fault conditions to allow for the dynamics in the system to be accurately observable. However, this is not optimal as fault conditions are infrequent and undesirable. The method detailed makes use of modal information which can be obtained via synchrophasor measurements to estimate the inertia during steady-state conditions. The results show that while the estimation is not accurate for individual buses, the values calculated for regional and system inertias are more accurate.

Original languageEnglish
Title of host publication2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
Volume2018-January
ISBN (Electronic)9781538622124
DOIs
Publication statusPublished - 2018 Jan 29
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: 2017 Jul 162017 Jul 20

Other

Other2017 IEEE Power and Energy Society General Meeting, PESGM 2017
CountryUnited States
CityChicago
Period17/7/1617/7/20

Keywords

  • Modal Information
  • Neural Networks
  • Parameter Estimation
  • Wide Area Measurements

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Schmitt, A., & Lee, B. J. (2018). Steady-state inertia estimation using a neural network approach with modal information. In 2017 IEEE Power and Energy Society General Meeting, PESGM 2017 (Vol. 2018-January, pp. 1-5). IEEE Computer Society. https://doi.org/10.1109/PESGM.2017.8274640