Probabilistic analysis of small-signal stability of large-scale power systems as affected by penetration of wind generation

Siqi Bu, W. Du, H. F. Wang, Z. Chen, L. Y. Xiao, H. F. Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

139 Citations (Scopus)

Abstract

This paper proposes a method of probabilistic analysis to investigate the impact of stochastic uncertainty of grid-connected wind generation on power system small-signal stability. The proposed method is analytical in contrast to the numerical method of Monte Carlo simulation which relies on large number of random computations. It can directly calculate the probabilistic density function (PDF) of critical eigenvalues of a large-scale power system from the PDF of grid-connected multiple sources of wind power generation, thus to determine the probabilistic small-signal stability of the power system as affected by the wind generation. In the paper, an example of 16-machine power system with three grid-connected wind farms is used to demonstrate the application of the proposed method. The results of probabilistic stability analysis of the example power system are confirmed by the Monte Carlo simulation. It is shown that the stochastic variation of grid-connected wind generation can cause the system to lose stability even though the system is stable deterministically. The higher the level of wind penetration is, the more the probability that the system becomes unstable could be. Hence indeed penetration of stochastically variable wind generation threatens stable operation of power systems as far as system small-signal stability is concerned.
Original languageEnglish
Article number6084851
Pages (from-to)762-770
Number of pages9
JournalIEEE Transactions on Power Systems
Volume27
Issue number2
DOIs
Publication statusPublished - 1 May 2012
Externally publishedYes

Keywords

  • Correlations
  • Gram-Charlier expansion
  • Monte Carlo simulation
  • power system small-signal stability
  • probabilistic analysis
  • probabilistic density function (PDF)
  • wind power generation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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