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Fault Detection and Classification using Deep Learning Method and Neuro-Fuzzy Algorithm in a Smart Distribution Grid
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  • Vinny Foba,
  • Alexandre Boum,
  • Camille Mbey,
  • Felix Yem
Vinny Foba
University of Douala

Corresponding Author:[email protected]

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Alexandre Boum
University of Douala
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Camille Mbey
University of Douala
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Felix Yem
University of Douala
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Fault detection is crucial in smart grid control and monitoring operations. The use of smart meters leads to appearance of a large amount of digital data whose conventional and chronological techniques are not efficient enough for processing and decision-making. In this paper, a novel data analysis model based on deep learning and neuro-fuzzy algorithm is proposed for detection and classification of faults in a smart grid. First, the Long Short Term Memory (LSTM) based deep learning model is applied for training the data samples extracted from the smart meters. Then, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented for fault detection and classification from the trained data. With this intelligent method proposed, single-phase, two-phase and three-phase faults can be identified using a restricted amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13-node network is used. The results indicate that the combined ANFIS-LSTM deep learning model outperforms existing machine learning methods in the literature in terms of accuracy for fault detection and classification.
28 Jun 2022Submitted to The Journal of Engineering
29 Jun 2022Submission Checks Completed
29 Jun 2022Assigned to Editor
18 Aug 2022Reviewer(s) Assigned
20 Jul 2023Review(s) Completed, Editorial Evaluation Pending
14 Sep 2023Editorial Decision: Revise Major
22 Sep 20231st Revision Received
25 Sep 2023Assigned to Editor
25 Sep 2023Submission Checks Completed
25 Sep 2023Reviewer(s) Assigned
30 Sep 2023Review(s) Completed, Editorial Evaluation Pending
05 Oct 2023Editorial Decision: Revise Minor
07 Oct 20232nd Revision Received
09 Oct 2023Submission Checks Completed
09 Oct 2023Assigned to Editor
09 Oct 2023Reviewer(s) Assigned
10 Oct 2023Review(s) Completed, Editorial Evaluation Pending
18 Oct 2023Editorial Decision: Accept