Asynchronous Traveling Wave-based Distribution System Protection with Graph Neural Networks

Jiménez-Aparicio, Miguel; Reno, Matthew J.; Wilches-Bernal, Felipe

doi:10.1109/kpec54747.2022.9814818

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…This work is interpreted as a demonstration of the feasibility of DL models for ultra-fast protection in distribution systems, paving the way for a future commercial application. The reality is that the models that are evaluated in this work are just scaled-down versions of the models that have been previously researched in [31]. This is because of the memory and computation constraints of the employed development board (which has one of the fastest microcontrollers that are available today).…”

Section: Discussionmentioning

confidence: 99%

“…Some recently developed methods use graph neural networks (GNNs), a method that achieves significant accuracies as both measurements and system topological information are used to provide a prediction [30]. The work in [31] proposed a distributed protection scheme that combines graph convolutional networks (GCNs), the most widely used type of GNNs, and TWs to predict the fault zone in the IEEE 34 nodes system. The reported accuracy is larger than 94%.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…This TW, ML error achieves less than a 1% error for fault distance prediction in the IEEE 34 node system. Other works show that ML models can achieve accuracies of more than 99% for fault location using a distributed approach with VOLUME 4, 2016 Graph Convolutional Networks (GCNs) [53]. However, the hardware implementation of GCNs requires communication among devices [54].…”

Section: Introductionmentioning

confidence: 99%

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Jimenez-Aparicio,

Patel,

Reno

et al. 2023

IEEE Access

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This work provides a detailed protection analysis of a fast, Traveling-Wave (TW), Machine-Learning (ML), local, non-directional, economic, and setting-less protection scheme. The goal is to provide an objective evaluation of how a data-driven TW protection scheme would perform when deployed on a power distribution system as a faster alternative to over-current (OC) protection. Fault simulations consider scenarios from none to high-penetration of renewable energy resources to show its suitability for future applications on Distributed Energy Resources (DER)-dominated distribution systems. A modified IEEE 34 node system with solar Photovoltaic (PV) in multiple locations is modeled in PSCAD/EMTDC. The TW, ML method's protection scheme is built upon an efficient signal-processing stage, using the Discrete Wavelet Transform, and scaled-down Random Forest models that classify the fault location into several protection zones. The analysis is focused on the quantification of the sensitivity and selectivity of the proposed protection scheme. Furthermore, estimations of the false trip probability and average unnecessary load loss are included, as these events may occur due to the misoperation and miscoordination of the proposed datadriven, signal-based relays. Results show high TW detection rates and fault location accuracies, which cause a small and bounded percentage of imprecise fault locations and unnecessary load loss. Similarly, the same IEEE 34 node system is modeled in OpenDSS and a custom OC protection scheme is designed. The comparison between both methods leads to the conclusion that TW, ML methods can be a significantly faster aid to traditional protection.

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Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data

2023

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Asynchronous Traveling Wave-based Distribution System Protection with Graph Neural Networks

Cited by 5 publications

References 9 publications

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

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

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data

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