Deep Neural Network based Wide-Area Event Classification in Power Systems

TL;DR

This paper develops a deep neural network classifier for wide-area event detection in power transmission systems using synchronized PMU data, validated on real U.S. grid data, showing high accuracy especially with ROCOF features.

Contribution

It introduces a DNN-based event classification method optimized with Bayesian hyperparameter tuning, utilizing diverse PMU data types for improved accuracy in power system event detection.

Findings

ROCOF as input yields best classification accuracy

Higher sampling rate PMUs improve classifier performance

Larger datasets enhance detection accuracy

Abstract

This paper presents a wide-area event classification in transmission power grids. The deep neural network (DNN) based classifier is developed based on the availability of data from time-synchronized phasor measurement units (PMUs). The proposed DNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset of the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency event, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The simulation results show that ROCOF as input feature gives the best classification performance. In addition, it is shown that the classifier trained with higher sampling rate PMUs and a larger dataset has higher accuracy.