Probabilistic small-disturbance stability assessment of uncertain power systems using efficient estimation methods

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    Abstract

    This paper presents comparative analysis of the performance of three efficient estimation methods when applied to the probabilistic assessment of small-disturbance stability of uncertain power systems. The presence of uncertainty in system operating conditions and parameters results in variations in the damping of critical modes and makes probabilistic assessment of system stability necessary. The conventional Monte Carlo (MC) approach, typically applied in such cases, becomes very computationally demanding for very large power systems with numerous uncertain parameters. Three different efficient estimation techniques are therefore compared in this paper - point estimation methods, an analytical cumulant-based approach, and the probabilistic collocation method - to assess their feasibility for use with probabilistic small disturbance stability analysis of large uncertain power systems. All techniques are compared with each other and with a traditional numerical MC approach, and their performance illustrated on a multi-area meshed power system. © 1969-2012 IEEE.
    Original languageEnglish
    Article number6774476
    Pages (from-to)2509-2517
    Number of pages8
    JournalIEEE Transactions on Power Systems
    Volume29
    Issue number5
    DOIs
    Publication statusPublished - 17 Mar 2014

    Keywords

    • Cumulant
    • eigenvalues
    • electromechanical oscillations
    • Monte Carlo (MC)
    • point estimation
    • probabilistic collocation method (PCM)
    • small disturbance stability
    • uncertainty

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