Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions

Abstract

Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator’s underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system.