Abstract: The comprehensive promotion of the new power system still faces multiple security challenges, especially the distributed power system is vulnerable to threats such as extreme weather, natural disasters and cyber attacks. These situations will lead to abnormal system fluctuations and equipment failures, making distributed power scheduling and control face a more complex situation. To cope with these challenges, the detection efficiency and accuracy of anomalous events are improved to assist the regulation decision-making techniques of distributed power systems and to improve the chain fault blocking capability. In this paper, we pro pose a distributed power control anomalous event detection model based on FHO-CatBoost. The model makes full use of CatBoost's powerful gradient framework and automatic processing of category features, and optimizes the model by adjusting the hyperparameters of the Fire Hawk Optimization (FHO) algorithm, which improves the performance of the model, and efficiently and accurately detects and identifies anomalous events. Experimental results demonstrate that the FHO-CatBoost model exhibits superior performance in accurately detecting different categories of abnormal events and overall performance. It outperforms other mainstream gradient boosting algorithms in multi-faceted performance evaluations, achieving an accuracy of 91.59%, which is a 6.58% improvement over the best CatBoost method. It demonstrates better performance and significant advantages in detecting abnormal events in distributed generations power control, providing important support for the safe operation of power systems.