Ensemble machine learning approach to identify excitation failure in synchronous generators

Abstract

Loss of Excitation (LOE) is a significant failure that occurs in synchronous generators, affecting both generator and power grid. The conventional LOE protection relays work based on the impedance measurement at generator terminal but they tend to mal-operate other than excitation failure like Stable Power Swing (SPoS). To achieve more reliable and accurate protection, a proper speedy discrimination of LOE failure from SPoS is indeed. This paper deals an ensemble machine learning methodology to identify LOE.In order to analyse LOE failure, generator dynamic model is considered and simulation is performed on IEEE-9 bus system. In this, various observations are formed for LOE, SPoS and other events under various loading scenarios. Discrete Wavelet Transform (DWT) analysis is employed on generator terminal voltage signal and Time-Frequency Domain (TFD) features are extracted to define unique characteristics of LOE failure. The ensemble Adaboost binary classifier is trained with feature matrix and tested. Finally, the ensemble model is established and identified LOE failure from SPoS in 1.517 s. Comprehensive simulation analysis confirmed the supremacy of the proposed method than other compared methods.