Fault Detection and Classification of Power Lines Based on Bayes–LSTM–Attention

As a critical component of the power system, transmission lines play a significant role in ensuring the safe and stable operation of the power grid. To address the challenge of accurately characterizing complex and diverse fault types, this paper proposes a fault detection and classification method for power lines that integrates Bayesian Reasoning (BR), Long Short-Term Memory (LSTM) networks, and the Attention mechanism. This approach effectively improves the accuracy of fault classification. Bayesian Reasoning is used to adjust the hyperparameters of the LSTM, while the LSTM network processes sequential data efficiently through its gating mechanism. The self-Attention mechanism adaptively assigns weights by focusing on the relationships between information at different positions in the sequence, capturing global dependencies. Test results demonstrate that the proposed Bayes–LSTM–Attention model achieves a fault classification accuracy of 94.5% for transmission lines, a significant improvement compared to the average accuracy of 80% achieved by traditional SVM multi-class classifiers. This indicates that the model has high precision in classifying transmission line faults. Additionally, the evaluation of classification results using the polygon area metric shows that the model exhibits balanced and robust performance in fault classification.