Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

TL;DR

This paper introduces a novel spatiotemporal feature learning approach using CNNs for accurately identifying causes of electromagnetic transient events in power grids, outperforming traditional methods.

Contribution

It develops an unsupervised CNN-based method that captures both spatial and temporal features for EMTE cause identification, validated through simulations and real-time data.

Findings

Effective in identifying various EMTE causes

Outperforms traditional threshold and energy-based methods

Validated on IEEE 30-bus and WSCC 9-bus systems

Abstract

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.