AINeutralarXiv – CS AI · Mar 27/1011
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FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
Researchers developed FaultXformer, a Transformer-based AI model that achieves 98.76% accuracy in fault classification and 98.92% accuracy in fault location identification in electrical distribution systems using PMU data. The dual-stage architecture significantly outperforms traditional deep learning methods like CNN, RNN, and LSTM, particularly in systems with distributed energy resources integration.
