Stability Analysis of Post-fault Power Angle Based on Relevance Vector Machine

Authors

Haiying Li, Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
Zhuocheng Xue, Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: Energy interconnection has become a hot topic in the current research.With the large-scale of new energy into the grid, the safe operation of power system is facing new challenges. On line fast stability analysis is one of the effective ways to solve this problem. Traditional power system transient safety analysis method is difficult to adapt to the on-line analysis, and the further development of large data processing and machine learning provides a new way of thinking. The classification model of relevance vector machine (RVM) based on Bayesian framework is used to design a kind of power angle classification strategy.The appropriate number of sampling points is found as the input of RVM, and the generator angle relation of the system is gotten to determine the unit operation. The method is verified in the New England 10-machine 39-node system and the results show a high degree of accuracy.

Recommended Citation

Li, Haiying and Xue, Zhuocheng (2019) "Stability Analysis of Post-fault Power Angle Based on Relevance Vector Machine," Journal of System Simulation: Vol. 30: Iss. 8, Article 34.
DOI: 10.16182/j.issn1004731x.joss.201808034
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss8/34

First Page

3098

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201808034

Last Page

3104

CLC

TM71

Recommended Citation

Li Haiying, Xue Zhuocheng. Stability Analysis of Post-fault Power Angle Based on Relevance Vector Machine[J]. Journal of System Simulation, 2018, 30(8): 3098-3104.

DOI

10.16182/j.issn1004731x.joss.201808034