An Attention Mechanism Based Algorithm of Identifying Vulnerable Spots of the Internet Data Center Power Systems

Abstract

With the rapid development of Mobile Internet and Big Data in recent years, the demand for data centers is increasing. The number of super large data centers (more than 1000 counters) has increased. This paper focus on the distribution system, who is responsible for providing a continuous, stable and uninterrupted power supply for the data center. In the event of a large-scale power outage, all business in the data center will be interrupted. Therefore, identifying which electrical equipment in the data center paly the key role in a large-scale power outage is extremely important. Considering the computational complexity of the algorithm, the validity of the vulnerable nodes, the intelligence of the algorithm, this paper proposes a two-layer fragile node identification method based on attention mechanism. The algorithm selects the topology structure and electrical parameters of the data center power distribution system, and considers the flow load of the nodes around each electrical device to form an expression vector. After the vulnerability ranking of the candidate node expression vectors, the k most vulnerable nodes are selected. The outline of the two-layer node identification method is as follows, the first layer calculates an alternative node sequence through a variety of graph-based fragile node identification methods, and the second layer re-calibrates the expression vector of each candidate node based on the attention mechanism and then sorts all candidate nodes. Through the simulation test of a real data center power distribution system, we can see that the proposed algorithm in this paper identifies the effectiveness of the vulnerable nodes in the data center power distribution system.