Determination and enhancement of probabilistic stability margin with load uncertainties

J. F. Zhang, C. T. Tse, K. W. Wang, X. Y. Bian, K. P. Wong, Siu Lau Ho, F. S. Lock

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Load margin is a reasonable measure to quantify the bifurcation-related instability, defined as the amount of additional load on a specified pattern of load increase that would cause system instability. If the initial operating point and load increase pattern are specific, the stability margin is specific and can be obtained easily. If the initial operating point is random, the stability margin is also a random variable. This paper adopts a probabilistic eigenvalue method to determine the stability margin under load uncertainty. With the assumption of load being of normal distribution, the computed eigenvalues distribution is also close to normal distribution, but the stability margin distribution is irregular. An effective and systematic probabilistic approach to assess the probabilistic margin is proposed. Monte Carlo simulations consisting of 10,000 samples are used as a reference solution for evaluation of the accuracy of the proposed method. In addition, power system voltage stabilizer is adopted to improve the probabilistic stability margin by a proposed optimization technique. The proposed methods are investigated on two test systems.
Original languageEnglish
Pages (from-to)19-27
Number of pages9
JournalInternational Journal of Electrical Power and Energy Systems
Volume45
Issue number1
DOIs
Publication statusPublished - 1 Feb 2013

Keywords

  • Probabilistic eigenvalue
  • Probabilistic stability margin
  • Voltage stability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Determination and enhancement of probabilistic stability margin with load uncertainties'. Together they form a unique fingerprint.

Cite this