An Empirical Evaluation of Transmission Line Fault Identification and Filtration Using Improved Deep Learning Strategy

Transmission line fault identification is crucial for maintaining the reliability and stability of electrical power grids. In this study, we propose an enhanced deep learning strategy for the precise detection and filtration of faults in transmission lines. Leveraging integrated DenseNet and Xception models, renowned for their effectiveness in handling complex data, we develop a robust fault detection system. The integrated DenseNet architecture facilitates efficient feature reuse and propagation through densely connected convolutional networks with skip connections, while the Xception model employs depthwise separable convolutions to capture spatial dependencies and reduce computational complexity. Pretrained on extensive datasets, these models automatically learn hierarchical features, enabling them to accurately identify faults in transmission lines. Through meticulous experimentation, our proposed strategy achieves an impressive accuracy of 98.2%, demonstrating its effectiveness in enhancing fault detection capabilities and ensuring the reliability of electrical power grids.