Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks

Miguel Jiménez-Aparicio (), Javier Hernández-Alvidrez, Armando Y. Montoya and Matthew J. Reno
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Miguel Jiménez-Aparicio: Sandia National Laboratories, Albuquerque, NM 87123, USA
Javier Hernández-Alvidrez: Sandia National Laboratories, Albuquerque, NM 87123, USA
Armando Y. Montoya: Sandia National Laboratories, Albuquerque, NM 87123, USA
Matthew J. Reno: Sandia National Laboratories, Albuquerque, NM 87123, USA

Energies, 2022, vol. 15, issue 20, 1-22

Abstract: This work proposes and develops an implementation of a fault location method to provide a fast and resilient protection scheme for power distribution systems. The method analyzes the transient dynamics of traveling waves (TWs) to generate features using the discrete wavelet transform (DWT), which are then used to train several graph convolutional network (GCN) models. Faults are simulated in the IEEE 34-node system, which is divided into three protection zones (PZs). The goal is to identify the PZ in which the fault occurs. The GCN models create a distributed protection scheme, as all nodes are able to retrieve a prediction. Given that message-passing between nodes occurs both during training and in the execution of the model, the resiliency of such schemes to communication losses was analyzed and demonstrated. One of the models, which only uses voltage measurements, was implemented on a Texas Instruments F28379D development board. The execution times were monitored to assess the speed of the protection scheme. It is shown that the proposed method can be executed in approximately a millisecond, which is comparable to existing TW protection in the transmission system. For experimental purposes, a DWT-based detection method is employed. A design of a setup to playback TWs using two development boards is also addressed.

Keywords: power system protection; fault location; traveling wave; discrete wavelet transform; graph convolutional networks; embedded real-time systems; digital signal processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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