Probabilistic stability of small disturbance in wind power system based on a variational Bayes and Lyapunov theory using PMU data

Abstract

Abstract With the distribution of the large‐scale emergence of wind power connected to the power system, the stability problem of small disturbance has become increasingly prominent, especially the low‐frequency oscillation phenomenon caused by the weak or the negative damping. Aiming at the problem of probabilistic stability of small disturbance in power system connected with wind power, this paper proposes a method of probabilistic stability based on a variational Bayes and Lyapunov theory using PMU data. This method aims to minimize the computational efficiency when the number of wide‐area time series PMU data is large. Firstly, PMU data are classified by the Random Forest Gini classification index. Secondly, a variational Bayesian framework is introduced to improve the computational efficiency. And then, to obtain the probabilistic stability criterion, this framework is combined with the direct Lyapunov theory. Finally, simulation results show that the method proposed in this paper greatly improves the system probabilistic stability of small disturbance. The comparison between the traditional Monte Carlo based on the Markov chain method and the method in this paper has been achieved, which could provide the theoretical support for the study of the low‐frequency oscillation significantly.

Keywords

• PMU data
• probabilistic stability
• small disturbance
• variational Bayes
• wind power system