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Communication Dans Un Congrès Année : 2022

An algorithm for the detection of DC series-arc faults using a Convolution Neural Network

Patrick Schweitzer
Ruobo Chu
  • Fonction : Auteur
Christophe Bonnet
  • Fonction : Auteur
  • PersonId : 949930
  • IdHAL : ch-bonnet
Yves Berviller
Serge Weber
Etienne Tisserand

Résumé

The detection of electrical failures and more specifically electric arcs faults detection becomes absolutely necessary because of their dangerousness. Unlike the main methods in the literature based on frequency analysis, we propose in this paper a method of detection based on CNN models (LeNet5-28*28 and 64*64 images). Series arc faults are produced in a circuit composed of a 270 Volt DC supply voltage and mainly resistive loads. The line current is recorded (dataset composed of about 11000 signatures with and without arc faults). We also consider the case of load variation without arc faults. The selected sections of the current signals are then transformed into 2D matrixes (images). Then, the network is trained, validated and tested using the dataset. The performance of this method is also studied and discussed. More precisely, the detection results are presented using a confusion matrix. Experimental results show that the method we propose can effectively detect arcing faults.
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Dates et versions

hal-04140571 , version 1 (25-06-2023)

Identifiants

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Patrick Schweitzer, Ruobo Chu, Yueyang Jiang, Christophe Bonnet, Yves Berviller, et al.. An algorithm for the detection of DC series-arc faults using a Convolution Neural Network. 2022 IEEE 67th Holm Conference on Electrical Contacts (HLM 2022), Oct 2022, Tampa, FL, United States. ⟨10.1109/HLM54538.2022.9969784⟩. ⟨hal-04140571⟩
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