Improved genetic algorithms for optimal power flow under both normal and contingent operation States

L. L. Lai, J. T. Ma, R. Yokoyama, M. Zhao

    Research output: Contribution to journalArticle

    265 Citations (Scopus)

    Abstract

    This paper presents an improved genetic algorithm (IGA) to solve the problem of optimal power flow. The GA, with the dynamical hierarchy of the coding system developed in this paper, has the ability to code a large number of control variables in a practical system within a reasonable length string. It is, therefore, able to regulate the active power outputs of generators, bus voltages, shunt capacitors/ reactors and transformer tap-settings to minimize the fuel costs. Two cases in the IEEE 30-bus system for both normal and contingent operation states have been studied. In the contingent state, the circuit outage is simulated in one branch which causes a power flow violation in the other branch. The IGA always finds the best results and eliminates operational and insecure violations.

    Original languageEnglish
    Pages (from-to)287-292
    Number of pages6
    JournalInternational Journal of Electrical Power and Energy Systems
    Volume19
    Issue number5
    Publication statusPublished - 1997 Jun

    Fingerprint

    Genetic algorithms
    Outages
    Capacitors
    Networks (circuits)
    Electric potential
    Costs

    Keywords

    • Artificial intelligence
    • Economic dispatch
    • Improved genetic algorithms
    • Optimal power flow

    ASJC Scopus subject areas

    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering

    Cite this

    Improved genetic algorithms for optimal power flow under both normal and contingent operation States. / Lai, L. L.; Ma, J. T.; Yokoyama, R.; Zhao, M.

    In: International Journal of Electrical Power and Energy Systems, Vol. 19, No. 5, 06.1997, p. 287-292.

    Research output: Contribution to journalArticle

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