Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

TL;DR

This paper introduces a CNN-based machine learning approach utilizing synchro waveform measurements for rapid and accurate event cause analysis in distribution networks, improving situational awareness and fault diagnosis.

Contribution

It presents a novel application of CNNs to analyze synchro waveform data for identifying various distribution network events, including faults and switching operations.

Findings

The method accurately classifies different event types.

It outperforms existing classifiers in speed and accuracy.

Effective with only one cycle of waveform data.

Abstract

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real-time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.