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Title: Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances

Abstract

Here, with the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multi-machine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.

Authors:
 [1];  [1];  [2];  [2];  [1];  [3]
  1. Hohai Univ., Nanjing (China)
  2. The Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1415905
DOE Contract Number:
AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Power Systems; Journal Volume: PP; Journal Issue: 99
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; energy function method; stability probability; stochastic averaging method; stochastic continuous disturbances; stochastic differential equations (SDEs); transient stability

Citation Formats

Ju, Ping, Li, Hongyu, Gan, Chun, Liu, Yong, Yu, Yiping, and Liu, Yilu. Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances. United States: N. p., 2017. Web. doi:10.1109/TPWRS.2017.2720687.
Ju, Ping, Li, Hongyu, Gan, Chun, Liu, Yong, Yu, Yiping, & Liu, Yilu. Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances. United States. doi:10.1109/TPWRS.2017.2720687.
Ju, Ping, Li, Hongyu, Gan, Chun, Liu, Yong, Yu, Yiping, and Liu, Yilu. Wed . "Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances". United States. doi:10.1109/TPWRS.2017.2720687. https://www.osti.gov/servlets/purl/1415905.
@article{osti_1415905,
title = {Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances},
author = {Ju, Ping and Li, Hongyu and Gan, Chun and Liu, Yong and Yu, Yiping and Liu, Yilu},
abstractNote = {Here, with the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multi-machine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.},
doi = {10.1109/TPWRS.2017.2720687},
journal = {IEEE Transactions on Power Systems},
number = 99,
volume = PP,
place = {United States},
year = {Wed Jun 28 00:00:00 EDT 2017},
month = {Wed Jun 28 00:00:00 EDT 2017}
}
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