Identification of Transmission Line Galloping Based on Improved Adaptive Mahony Filter

With the continuous expansion of power transmission networks, galloping accidents of transmission lines occur frequently, often causing serious consequences such as hardware damage and conductor short circuits. To accurately detect and identify line galloping, this paper proposes a galloping identification algorithm based on an improved adaptive Mahony filter. The method uses six-axis inertial sensor data, combining angular velocity and acceleration measurements to estimate conductor attitude in real time. An adaptive Mahony filter enhances attitude estimation under dynamic conditions, while a frequency-domain split integration strategy mitigates drift and noise during double integration, enabling precise displacement and amplitude estimation. Simulation results demonstrate that the proposed algorithm achieves high accuracy in attitude, velocity, and displacement estimation, with galloping amplitude errors below 10%, satisfying the technical requirements for transmission line monitoring. This study provides a robust and cost-effective algorithm-level solution for sensor-based transmission line galloping identification.