Fault diagnosis of transmission lines via adaptive modal filtering and an enhanced convolutional neural network synergistic approach

To address the challenges of high concealability and difficulty in identifying minor damage in transmission lines, as well as low fault diagnosis accuracy under strong noise conditions, a novel fault diagnosis method for transmission lines is proposed. A data processing method based on adaptive modal filtering is proposed by combining a variational constraint model with an adaptive frequency band extraction strategy. Subsequently, by leveraging the concept of the generalized Fourier transform, pseudopeak effects near modal frequencies are suppressed, achieving thorough noise signal filtering without altering the intrinsic state characteristics of the transmission lines. For fault diagnosis, a convolutional neural network enhanced with an attention module is constructed, and a fault diagnosis model integrated with bidirectional long short-term memory (BiLSTM) is proposed. By embedding a convolutional block attention module, network weights are dynamically adjusted to enhance feature representation in both channel and spatial dimensions. Additionally, the introduction of BiLSTM strengthens the model’s ability to process time series data. Finally, the proposed method is validated on a conductor vibration test platform, demonstrating its high diagnostic accuracy and superior performance in noisy environments compared with other models.