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Network attack detection method for power system terminal based on improved random forest

With the rapid development of smart grids and the continuous deepening of informatization, people’s requirements for power stability are getting higher and higher. Large-scale blackouts have brought great losses and inconveniences to the society, economy and people’s lives, and especially brought great challenges to the service level of power companies. In view of the fact that it is difficult for the power grid to detect which factors caused the power outage after a power outage accident occurs, this paper proposes a method for detecting the cause of a power outage accident based on an improved random forest. First, the original data is preprocessed, and the data set is balanced by the Smooth oversampling method; then, the ReliefF method is used to select the optimal feature of the processed data to obtain the feature importance ranking, and screen to obtain the strong influencing factors related to power outages; Finally, the optimal feature subset after feature selection is used as the training set to construct a power outage accident cause detection model based on improved random forest to realize timely detection of power outage causes after a power outage accident occurs. Through experiments on historical blackout data, the effectiveness and feasibility of the proposed method are verified.