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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mouckomey, Etienne Francois
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Computer Science & IT Engineering

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A smart grid fault detection using neuro-fuzzy deep learning algorithm Mouckomey, Etienne Francois; Bikai, Jacques; Mbey, Camille Franklin; Boum, Alexandre Teplaira; Souhe, Felix Ghislain Yem; Kakeu, Vinny Junior Foba
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5096-5105

Abstract

This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Co-Authors Bikai, Jacques Boum, Alexandre Teplaira Kakeu, Vinny Junior Foba Mbey, Camille Franklin Souhe, Felix Ghislain Yem