A Novel Approach for Rotor Angle Stability Prediction of Power Systems with High Wind Power Penetration

Power systems are becoming more open, stochastic and dynamic nonlinear as the integration of renewable energy sources are keeping increasing. On the other hand, power systems are usually confronted with various weather conditions and fortuitous events that may lead to incidents causing instability of the networks. This research will propose an on-line transient stability prediction approach for power systems with high penetration of renewable energy, which aims at fast detecting the potential instabilities in systems thus saving more time for remedial controls and in further, prevents unintended islanding, cascading outages, and widespread blackouts. Applications of statistical machine learning in power system stability analysis and control would be studied in this research to support the achievement of a safe, reliable, and sustainable power grid.

Faculty Supervisor:

Chi Yung Chung

Student:

Partner:

Imperial College London

Discipline:

Engineering

Sector:

Education

University:

University of Saskatchewan

Program: