Fast online identification of power system dynamic behaviour

Papadopoulos, Panagiotis N. and Bhui, Pratyasa and Milanovic, Jovica V. and Senroy, Nilanjan (2017) Fast online identification of power system dynamic behaviour. In: 2017 IEEE Power and Energy Society General Meeting, PESGM 2017, 2017-07-16 - 2017-07-20.

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Abstract

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

ORCID iDs

Papadopoulos, Panagiotis N. ORCID: https://orcid.org/0000-0001-7343-2590

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• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	71723
  Dates:	Date Event 20 July 2017 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Pure Administrator
  Date deposited:	06 Mar 2020 16:16
  Last modified:	19 Nov 2024 01:29
  Related URLs:	https://www.research.manchester.ac.uk/po...
  URI:	https://strathprints.strath.ac.uk/id/eprint/71723