Wide Area Measurement-based Transient Stability Prediction using Long Short-Term Memory Networks

A fast and accurate transient stability assessment is essential for maintaining the reliability and integrity of the power system where quick corrective control actions are to be taken to prevent cascading outages. In this work, two separate classifiers based on long short-term memory networks are proposed to predict the stability status of a power system after being subjected to a credible fault. These classifiers use the immediate post-fault measurements of bus voltage magnitudes and rate of change of frequencies obtained from phasor measurement units during the first few cycles of the post-fault period. The proposed method does not assume that every bus is equipped with a PMU. It is found out that, even with only 30% of buses equipped with PMUs, the proposed classifiers are able to predict the stability status with an accuracy above 97%. Robustness of the classifiers is also investigated under noisy conditions and against the scenario of missing PMU measurements. A comparison between the two proposed classifiers is conducted by means of a t-test.