A hybrid neural network-decision tree-based method for transient stability assessment

Phasor Measuring Units (PMUs) using synchronization signals from Global Positioning System (GPS) satellite system have evolved into mature tools for power system operation and control. For power system transient stability assessment, a computationally efficient way of processing real-time measurements to determine whether an evolving swing will ultimately be stable or unstable is required. A hybrid pattern classifier that combines neural networks and decision trees has been used to assess a 12-generator power system based on phasor measurements with classification rates of over 99% for the training set and over 94% for the test set.