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Recent Patents on Mechanical Engineering

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ISSN (Print): 2212-7976
ISSN (Online): 1874-477X

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Research Article

Research on Power Angle Characteristics of One-Machine Infinite-Bus Power Systems of Mixed Gauss and Poisson Stochastic Excitation

Author(s): Lijuan Li, Yuange Li, Chuang Xiao*, Xinhui Zheng and Qing Li

Volume 17, Issue 3, 2024

Published on: 30 January, 2024

Page: [171 - 180] Pages: 10

DOI: 10.2174/0122127976270697231219054728

Price: $65

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the diversification and complexity of stochastic excitation, and the traditional single-excitation stochastic model is no longer applicable.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diversification and complexity of stochastic excitation and makes the traditional stochastic model of single excitation no longer applicable.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.

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