Abstract: In early warning of the key nodes for power grid project, the unique outlier characteristics are considered. In order to solve the current problems of large risk warning errors, low accuracy and stability. An early warning algorithm of key nodes of power grid project based on outlier detection is proposed. Using the measurement index of outlier early warning, the task importance index of static outlier and outlier is calculated. Using analytic hierarchy process and entropy weight method, combined with multi index fusion weighting, the characteristics of key outlier nodes are extracted to complete the identification of key nodes. K-means is used to cluster the early warning process of key nodes of power grid. The fusion weight characteristics of key nodes of power grid are introduced into the outlier detection system to analyze the data output results, obtain the optimal clustering value, and realize the early warning of key nodes of power grid project. The experimental results show that the proposed method has high stability and accuracy, and can effectively reduce the risk of early warning error. 

Key words: power grid project')">

power grid project, importance, outlier detection, identification of key nodes, risk warning