A Review of Power System Instability Prediction Methods Using Phasor Measurement Unit Data

Abstract

Electrical power systems face daily challenges due to changes in load and generation. These changes influence the stability of the power system as well as the secure operation of the electrical network. Phasor Measurement Units (PMUs) provide the means to achieve more responsive and accurate monitoring of instabilities on the electrical network than a traditional SCADA system. Prediction of power system instability is essential for situational awareness and the reliable operation of the power system. This research presents a review of instability prediction methods using PMU data. These methods include neural networks, auto-regression, FFT, One Class Support Vector as well as Parallel Detrending Fluctuation Analysis. Performance of these methods is analyzed in terms of accuracy and application.