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Fault localization method for power distribution systems based on gated graph neural networks

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Abstract

Fault localization is a key task on power systems operation and maintenance. When it comes to distribution networks, the problem is especially challenging due to the non-homogeneous characteristics and unique topology of each feeder. This paper presents a method based on gated graph neural network for automatic fault localization on distribution networks. The method aggregates problem data in a graph, where the feeder topology is represented by the graph links and nodes attributes can encapsulate any selected information such as operated devices, electrical characteristics and measurements at the point. The main advantage of the proposed solution is that it is immune to network reconfiguration and allows the use of a single trained model on multiple feeders. An experiment was conducted with faults simulated on 10 different feeders, all of them based on actual distribution feeders. The results shows that the model is able to generalize the correlations learned on training to correctly predict the fault region in most cases, even on a feeder it has not seen before.

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Acknowledgements

The authors would like to thank Pró-Reitoria de Pesquisa (PRPq) of the Universidade Federal de Minas Gerais (UFMG), FAPEMIG, CNPq, and CAPES; Companhia Energética de Minas Gerais (CEMIG) for providing data from its distribution system used in the experiments, and Eng. Ezequiel Campos Pereira of CEMIG’s Electrical System Planning and Expansion Engineering department, for the support with the simulations.

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  1. Escola de Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Belo Horizonte, Minas Gerais, Brazil

    Jonas Teixeira de Freitas & Frederico Gualberto Ferreira Coelho

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  1. Jonas Teixeira de Freitas
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  2. Frederico Gualberto Ferreira Coelho
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Correspondence to Jonas Teixeira de Freitas.

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de Freitas, J.T., Coelho, F.G.F. Fault localization method for power distribution systems based on gated graph neural networks. Electr Eng 103, 2259–2266 (2021). https://doi.org/10.1007/s00202-021-01223-7

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  • DOI: https://doi.org/10.1007/s00202-021-01223-7

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