Power Grid Online Surveillance through PMU-Embedded Convolutional Neural Networks

Abstract

Power grid operation continuously experiences state transitions caused by the internal and external uncertainties, e.g., equipment failures and weather-driven faults. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems captured by the phasor measurement units (PMUs) and intelligent electronic devices (IEDs) embedded with PMU functionality, e.g., digital relays and fault recorders. The PMU should be, hence, equipped with either one synchrophasor estimation algorithm (SEA) that is accurate and robust to many different types of signals any time across the network, or should adaptively select the promising SEA, among an embedded suite of algorithms. This paper proposes a PMU-embedded framework that can ensure real-time grid surveillance and potentially enables adaptive selection of SEA for more accurate synchrophasor estimation. Our proposed framework is consisted of two components: (i) a pseudo continuous quadrature wavelet transform (PCQ-WT) algorithm using a modified Gabor wavelet transform, which generates the featured-scalograms; and (ii) a convolutional neural network (CNN), that classifies the events based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves state-of-the-art classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real-time can be realized.