Anomaly attribute mining method for topological nodes of power system based on graph theory

Due to the large scale, high dimension and time series characteristics of power system data, and the normal samples far exceed the abnormal samples, the sample imbalance phenomenon occurs, and it is difficult to mine the abnormal attributes of nodes. Therefore, a method of mining abnormal attributes...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-03, Vol.2728 (1), p.12087
Main Authors: Tan, Huijuan, Lu, Jiangang, Zhao, Ruifeng, Dai, Yue, Wu, Yuezhou
Format: Article
Language:English
Subjects:
Adaptive algorithms
Clustering
Energy consumption
Graph theory
Mining
Nodes
Topology
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Summary:Due to the large scale, high dimension and time series characteristics of power system data, and the normal samples far exceed the abnormal samples, the sample imbalance phenomenon occurs, and it is difficult to mine the abnormal attributes of nodes. Therefore, a method of mining abnormal attributes of topological nodes of power systems based on graph theory is developed. The logical relation between nodes is analyzed by graph theory, and directed graph and undirected graph are obtained. The topology structure of the power system is constructed, the noise in data is removed by the adaptive clustering algorithm, the weight of topological nodes is set, and the abnormal malicious attacks of topological nodes are discovered by the double threshold method. The experimental results show that the proposed method can accurately detect the abnormal topological nodes in the network, perform better in the energy consumption of the neighbor topological nodes, and greatly reduce the energy consumption of the neighbor topological nodes during mining.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2728/1/012087