A Deep Recurrent Learning Framework for Multi-Class Microgrid Fault Classification Using LSTM and Bi-LSTM Models
Year: 2026
Type: article
Abstract: Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems.
Source: Eng—Advances in Engineering
DOI: 10.3390/eng7030143
Authors: Rakesh Sahu, Pratap Kumar Panigrahi, Deepak Kumar Lal, Rudranarayan Pradhan, Chandrakanta Mahanty
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W7140108888