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Title: Analytic Estimation Method of Forced Oscillation Amplitude Under Stochastic Continuous Disturbances

Journal Article · · IEEE Transactions on Smart Grid
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [1]; ORCiD logo [2]
  1. Hohai Univ., Nanjing (China). College of Energy and Electrical Engineering
  2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
  3. Florida Atlantic Univ., Boca Raton, FL (United States). Dept. of Computer and Electrical Engineering and Computer Science, Inst. for Sensing and Embedded Network Systems Engineering
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This paper proposes an analytic method for the amplitude estimation of the forced oscillation under stochastic continuous disturbances (SCDs). There is considerable interest in the integration of intermittent renewable energy resources into a power system, which leads to the power system in an environment with SCDs. Thus, the forced oscillation issues under SCDs are of concern. In this paper, the SCDs are described as continuous-time stochastic processes, and then the systems under SCDs are modeled by stochastic differential algebraic equations. Furthermore, by employing stochastic theory, an efficient analytic method for calculating the statistics of system states during the forced oscillation is proposed. Moreover, a novel forced oscillation phenomenon, which presents system states oscillate with stationary distributions under SCDs, is clearly characterized and theoretically explained. Based on the proposed analytic method, a scheme is put forth to estimate amplitudes of system states during forced oscillation under SCDs. Finally, compared to Monte Carlo simulation, the proposed scheme has two significant advantages. First, the proposed scheme offers a fast and effective solution for engineering applications. Second, the impact mechanism of SCDs on forced oscillation in power system is clearer, by the proposed scheme.

Research Organization:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1564164
Journal Information:
IEEE Transactions on Smart Grid, Vol. 10, Issue 4; ISSN 1949-3053
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

Cited By (1)

Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory journal December 2019

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