Disruptive Event Classification using PMU Data in Distribution Networks

TL;DR

This paper presents a data-driven framework utilizing PMU data for classifying disruptive events in distribution networks, specifically distinguishing between malfunctioned equipment and normal load changes, verified through simulations.

Contribution

It introduces a novel PMU-based classification framework for disruptive events in distribution grids, applying PCA+SVM and autoencoder+softmax algorithms for improved detection.

Findings

High classification accuracy demonstrated in simulations

Effective distinction between malfunctioned equipment and load changes

Framework applicable for preventive maintenance strategies

Abstract

Proliferation of advanced metering devices with high sampling rates in distribution grids, e.g., micro-phasor measurement units ({\mu}PMU), provides unprecedented potentials for wide-area monitoring and diagnostic applications, e.g., situational awareness, health monitoring of distribution assets. Unexpected disruptive events interrupting the normal operation of assets in distribution grids can eventually lead to permanent failure with expensive replacement cost over time. Therefore, disruptive event classification provides useful information for preventive maintenance of the assets in distribution networks. Preventive maintenance provides wide range of benefits in terms of time, avoiding unexpected outages, maintenance crew utilization, and equipment replacement cost. In this paper, a PMU-data-driven framework is proposed for classification of disruptive events in distribution networks. The two disruptive events, i.e., malfunctioned capacitor bank switching and malfunctioned regulator on-load tap changer (OLTC) switching are considered and distinguished from the normal abrupt load change in distribution grids. The performance of the proposed framework is verified using the simulation of the events in the IEEE 13-bus distribution network. The event classification is formulated using two different algorithms as; i) principle component analysis (PCA) together with multi-class support vector machine (SVM), and ii) autoencoder along with softmax classifier. The results demonstrate the effectiveness of the proposed algorithms and satisfactory classification accuracies.