Disruptive Event Classification using PMU Data in Distribution Networks
This addresses preventive maintenance for distribution grid assets to avoid outages and reduce costs, but is incremental as it applies existing methods to a specific domain.
The paper tackles classification of disruptive events in distribution networks using PMU data, achieving satisfactory classification accuracies for distinguishing malfunctioned capacitor bank switching and regulator OLTC switching from normal load changes.
Proliferation of advanced metering devices with high sampling rates in distribution grids, e.g., micro-phasor measurement units (μPMU), 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.