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Gabriel Aparecido Fonseca
Departamento de Engenharia – Universidade Federal Lavras, MG
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Danton Diego Ferreira
Departamento de Engenharia – Universidade Federal Lavras, MG
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Flávio Bezerra Costa
Escola de Ciências e Tecnologia – Universidade Federal do Rio Grande do Norte, RN
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Aryfrance Rocha Almeida
Departamento de Engenharia Elétrica – Universidade Federal do Piauí, PI
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Robson Rosserrani de Lima
Faculdade de Engenharia - Universidade Federal de Juiz de Fora, MG
Keywords:
Rocket, MiniRocket, fault classification, transmission lines, notch filter, random forests
Abstract
Power transmission lines are components highly susceptible to faults. Several works have already explored the use of computational intelligence, signal processing and other techniques in the construction of protective methods for quick verification and action in the occurrence of transmission line fault. Many of these works focus on approaches using signal processing such as Fourier or wavelet transforms. With the advance of machine learning, some techniques began to be used in this area with success. This work focuses on the offline classification of ten (AG, BG, CG, AB, AC, BC, ABG, ACG, BCG and ABC) types of faults that arise when a short circuit occurs in the transmission line, investigating the use of classical techniques such as notch filter and random forests. For comparative purposes, recently created techniques, called random convolutional kernel transform (Rocket) and MiniRocket, were used to extract features in time series and good results were obtained in the identification of faults that occurred in the transmission line. As a result of this paper, accuracies greater than 93% were obtained considering up to 1/16 cycle post-fault. For signals with 1 and 1/2 cycle post- fault, accuracies higher than 97% were obtained.
