A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

Kai Hou, Hongjie Jia, Xiandong Xu, Zhe Liu, Yilang Jiang

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Original languageEnglish
Pages (from-to)738-748
Number of pages11
JournalIEEE Transactions on Power Systems
Volume31
Issue number1
Early online date27 Jan 2015
DOIs
Publication statusPublished - Jan 2016

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

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