Probabilistic framework for online identification of dynamic behavior of power systems with renewable generation

Panagiotis N. Papadopoulos, Tingyan Guo, Jovica V. Milanović

Research output: Contribution to journalArticle

17 Citations (Scopus)
31 Downloads (Pure)

Abstract

The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation.
Original languageEnglish
Pages (from-to)45-54
Number of pages10
JournalIEEE Transactions on Power Systems
Volume33
Issue number1
Early online date28 Mar 2017
DOIs
Publication statusPublished - 31 Jan 2018

Keywords

  • generators
  • network topology
  • power system dynamics
  • power system stability
  • topology
  • training
  • uncertainty
  • clustering
  • data analytics
  • decision trees
  • phasor measurement units
  • probabilistic transient stability
  • renewable generation

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