Early Prediction of Transient Instabilities Based on Pre-Fault Phasor Measurements using Decision Tree-based Methods

In recent years, many blackouts occurred in power systems of different parts of the world, affecting millions of people and causing great economic losses. Power system stability, which has a critical role in the design and operation of electrical power systems, maintains its importance today. Monitoring the stability status of a power system in real time is regarded as a primary task in preventing system blackouts. This allows of a sufficient amount of time to take appropriate corrective control actions. In this study, the pre-fault voltage magnitudes and angles taken from the phasor measurement units (PMU), clearing time of the fault and topology information of the transmission line that has been tripped for clearing the fault are used to predict the transient instabilities by two different methods based on the decision trees. The success and the effectiveness of the proposed machine learning models are shown as they are applied to the 127-bus Western Systems Coordinating Council (WSCC) test system