Fault Diagnosis in Electrical Power Networks Using a Novel Hybrid Deep Learning Model
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Faults in high-voltage transmission lines (HVTLs) represent a significant challenge to the stability and reliability of power grids. Therefore, fault detection (FD) and fault classification (FC) in HVTLs are crucial for ensuring rapid power supply restoration and preventing electrical energy losses. In this paper, a novel hybrid deep learning model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) techniques with the Harris Hawks Optimization (HHO) algorithm is proposed for accurate fault detection and classification. Three power grids, rated at 132 kV and 50 MW and extending over a total length of 150 km, were designed in the MATLAB environment to generate the training data. The data were then processed using Python to train the proposed model. Subsequently, HHO was employed to determine the optimal parameters of the LSTM-GRU model, including the number of neurons, learning rate, dropout rate, and mean squared error. To evaluate the model’s performance, four statistical measures were used: the confusion matrix, precision, recall, and F1-score. The results show that the LSTM-GRU-HHO model achieved the highest FD and FC accuracies of 99.90% and 99.84%, respectively, outperforming the LSTM, GRU, and LSTM-GRU models, as well as related models reported in previous studies.
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