  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.
