A Fuzzy Logic-Based Adaptive Fusion Algorithm for GNSS/IMU in Conductor Galloping Monitoring

To address the long-standing trade-off between high accuracy and low power consumption in existing GNSS/IMU fusion systems for transmission line galloping monitoring, this paper proposes an intelligent adaptive fusion algorithm based on Fuzzy Logic Control (FLC). The proposed method designs a dual-input fuzzy controller that dynamically schedules the GNSS update process of a two-stage Extended Kalman Filter (EKF), using external motion intensity and filter state uncertainty as decision variables. This enables an intelligent, demand-driven allocation of sensing resources. To evaluate the effectiveness and robustness of the proposed approach, a series of comparative experiments were conducted on a high-fidelity simulation platform under various wind-speed conditions, using fixed-frequency and conventional threshold-triggered methods as benchmarks. Experimental results demonstrate that the proposed FLC-based algorithm achieves nearly the same tracking accuracy as the high-power 10 Hz baseline while reducing GNSS updates by more than 40 %. Moreover, it delivers significantly higher accuracy compared with the 5 Hz baseline under similar power budgets. These findings confirm that the FLC-based adaptive scheduling strategy effectively optimizes the accuracy-power trade-off in GNSS/IMU fusion, providing a feasible and generalizable solution for long-term, energy-efficient online monitoring of transmission line galloping.