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This paper presents a machine learning method for event cause analysis toenhance situational awareness in distribution networks. The data streams arecaptured using time-synchronized high sampling rates synchro waveformmeasurement units (SWMU). The proposed method is formulated based on a machinelearning method, the convolutional neural network (CNN). This method is capableof capturing the spatiotemporal feature of the measurements effectively andperform the event cause analysis. Several events are considered in this paperto encompass a range of possible events in real distribution networks,including capacitor bank switching, transformer energization, fault, and highimpedance fault (HIF). The dataset for our study is generated using thereal-time digital simulator (RTDS) to simulate real-world events. The eventcause analysis is performed using only one cycle of the voltage waveforms afterthe event is detected. The simulation results show the effectiveness of theproposed machine learning-based method compared to the state-of-the-artclassifiers.

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements