Power system security pattern recognition based on phase space visualization

This paper studies stability pattern recognition of power systems using phase space and proposes a new online scheme to monitor phase-space curves of critical variables obtained from wide-area measurement system data, and recognize precursor signals that indicate potential instability or cascading outages. A potential energy-like function is defined and used as a single critical variable, by which significant changes in pattern of system security can be revealed. Selection criterions of pattern recognition tools are also discussed. Simulations show that the proposed scheme provides a way to offline study precursor signals for a particular power system and online recognize abnormal patterns before the system loses stability.