Data-Driven Event Location Identification Without Knowing Network Parameters Using Synchronized Electric-Field and Current Waveform Data

Abstract

Event location identification is a challenging task in power distribution feeders due to limited number of measurement devices. Another challenge is the lack of access to reliable information on network parameters. This paper proposes a new method to address both challenges. We identify the location of the events in distribution feeders using synchro-waveform measurements from a group of line-mounted sensors, which are inexpensive and easy to install. Importantly, we do not require any prior knowledge about the network parameters, i.e., the impedance of the distribution lines and the loading at each bus. The sensors in this study measure the time-domain waveforms for electric field and current; they do not measure voltage. First, the voltage waveform is approximated from the available electric field waveform measurement. Next, the network parameters are estimated by a novel event-based method using data from a few locationally scarce synchro-waveform measurements. Finally, the location of the event is identified by analyzing a data-driven reconstructed circuit model. The method is applied to real-world measurements from a distribution feeder in the United States. Despite not using any knowledge about the network parameters and also using measurements from only a few sensors, the results demonstrate the accuracy and consistency of the proposed framework in identifying the location of the events.