A High-Voltage Transmission Line Fault-Location Approach Integrating Mechanism Features and CBAM-CNN

High-voltage transmission lines are critical carriers of electric power, and rapid and accurate fault location is essential for secure and stable power-system operation. This paper proposes a fault-location method that integrates distributed-parameter physics-based features with a convolutional block attention module-based convolutional neural network (CBAM-CNN). Voltages and currents measured at both line terminals are transformed into modal quantities, after which a distributed-parameter line model is used to derive compensated-voltage waveforms along the line and construct a physically meaningful feature matrix. The CBAM-CNN is trained offline using multiple waveform samples, and the fault location is determined from the minimum similarity value among the observation points. Under identical test settings, the proposed model achieves an overall mean absolute fault-location error of 0.0746 km across four simulated test locations, representing reductions of 79.23%, 85.15%, and 68.44% relative to CNN, SE-CNN, and ECA-CNN, respectively. For a field record whose operation and maintenance record places the fault 51.0 km from the M terminal, the proposed model estimates 51.2089 km, corresponding to an absolute error of 0.2089 km. These results demonstrate high fault-location accuracy within the scope tested and provide preliminary evidence of applicability to field recordings.