Robust Stochastic Stability Analysis Method of DFIG Integration on Power System Considering Virtual Inertia Control

Robust Stochastic Stability Analysis Method of DFIG Integration on Power System Considering Virtual Inertia Control

Abstract:

Concerning the stochastic excitation caused by wind power fluctuation and the stochastic parameter of virtual inertia caused by wind speed uncertainty, a power system stability analysis method considering the Wiener noise is proposed in this paper. First, based on the reduced-order model of DFIG retaining virtual inertia control and phase-locked loop (phase-locked loop) dynamics, the analytic function relationship between stochastic parameters and elements in the state matrix is derived using the sensitivity analysis method; thus, the model of interconnected system with stochastic parameters considering the Wiener noise is established. And by constructing the Lyapunov functional containing the model of stochastic parameters, the linear matrix inequality (LMI) that satisfies the robust stochastic stability criterion is derived. Furthermore, by transforming the solving of LMI to a feasibility problem, the stability of system is identified. And then, by calculating online system instability probability under the current operating condition, the probability stability analysis method is proposed. On this basis, a system stochastic stability degree index is defined in this paper, which quantifies the effect of stochastic parameters on system small-signal stability, and could provide more effective information for the safe and stable operation of power system. Simulation tests on the IEEE four-generator two-area system and the New England 10-machine 39-bus system verify that the proposed method could effectively identify the small-signal stability of power system under the joint influence of stochastic parameter and stochastic external excitation, and is applicable to complex system with multiple generators.
Published in: IEEE Transactions on Power Systems Volume: 32Issue: 5, Sept. 2017 )
Page(s): 4069 – 4079
Date of Publication: 24 January 2017
 ISSN Information:
INSPEC Accession Number: 17121803
Publisher: IEEE

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