Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems

• Published in: Protection and control of modern power systems Vol. 6; no. 1
• Main Authors: Li, Lijuan, Chen, Yongdong, Zhou, Bin, Liu, Hongliang, Liu, Yongfei
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.12.2021; Power System Protection and Control Press
• Subjects: Algorithms; Alternative energy sources; Dynamic models; Dynamic stability; Electrical Machines and Networks; Energy; Energy resources; Energy Systems; Excitation; Linearization; Methods; Numerical analysis; Original Research; Power Electronics; Predictor-corrector methods; Random variables; Renewable and Green Energy; Renewable energy sources; Renewable resources; Stability analysis; Stochastic models; Systems stability; Threshold condition; Power system stability; Stochastic dynamic model; Stochastic excitation; Stochastic processes
• ISSN: 2367-2617; 2367-0983
• DOI: 10.1186/s41601-021-00198-8

With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.