Affiliation:
1. College of Automation and Electronic Information, Xiangtan University, Xiangtan, 411105, China
Abstract
Background:
A high percentage of renewable energy and a high percentage of power
electronic devices are connected to the power system, which leads to the diversification and
complexity of stochastic excitation, and the traditional single-excitation stochastic model is no
longer applicable.
Objective:
The study aimed to solve the problem that the high proportion of renewable energy
and the high proportion of power electronic equipment are connected to the power system, which
leads to the diversification and complexity of stochastic excitation and makes the traditional stochastic
model of single excitation no longer applicable.
Methods:
Firstly, stochastic differential equations for power systems have been modelled with
mixed Gaussian white noise and Poisson white noise excitation. Secondly, the Milstein-Euler
predictor-corrector method has been developed to solve the stochastic differential equation model
of the power system. Finally, the influence of Gauss white noise and Poisson white noise on
the power system stability under different excitation intensities has been analyzed. The rationality
and correctness of the model have been verified by the simulation of a one-machine infinitebus
(OMIB) system.
Results:
The stochastic differential equation model of a power system with Gauss white noise
and Poisson white noise excitation has been established and its angle stability has been analyzed.
Increasing the Gaussian white noise and Poisson white noise excitation intensity can lead to an
increase in the fluctuation of the power angle curve, as well as an increase in the standard deviation
and expected value of the power angle mean curve, which may decrease the stability of the
power system.
Conclusion:
This study provides a reference for stochastic power systems modeling and efficient
simulation, and has important application value for power system stability assessment and
safety evaluation as well as related patent applications.
Funder
National Natural Science Foundation of China
Publisher
Bentham Science Publishers Ltd.
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