In the context of large-scale renewable energy integration

the intermittency and volatility of renewable energy generation superimposed with load variations have exacerbated voltage fluctuation issues. To accurately locate disturbance sources in distribution networks

this paper proposes a method for voltage fluctuation source localization based on Bayesian-optimized XGBoost. The method extracts the disturbance energy

system trajectory slope

equivalent impedance

and the real part of current as key localization features. The XGBoost ensemble classification learning is employed to mine the mapping relationship between disturbance feature quantities and disturbance location information

constructing a multi-feature fusion voltage fluctuation source localization model. The Bayesian probability prediction is utilized to realize adaptive optimization of the XGBoost model hyperparameters

further enhancing the model's accuracy and robustness. Simulation results on the IEEE 33-node system demonstrate that the proposed method achieves a fluctuation source localization accuracy of up to 99.6%

significantly outperforming conventional methods. This provides an effective assurance for the stable operation and high-quality power supply of modern distribution systems.