基于子网划分的电网关键节点识别

结合电网拓扑结构和潮流追踪技术，提出一种基于子网划分的电网关键节点识别方法。首先，根据发电机节点的邻域信息和功率将发电机节点划分为不同的子集，然后根据电网的系数分配矩阵将负荷节点划分到为其提供最大功率的发电机节点子集中，完成子网划分。接着采用多属性决策法对每个子网的节点进行排序，进一步改进并计算每个子网的结构系数，作为衡量子网重要性的指标。根据子网重要性，从每个子网中提取特定比例的候选关键节点，对这些候选节点依据多属性决策法重新排序，得到关键节点的最终排序。以IEEE14、IEEE57和IEEE118三种节点系统为例进行分析，得到各个系统的子网划分结果和各个标准网络的重要节点排序结果。采用本文方法、PageRank法和多属性决策法分别进行关键节点排序，并对排序靠前的关键节点进行级联故障性能实验和网络效能实验。实验表明，本文算法选择的关键节点对整个网络的传播性能影响最大，优于其他两种关键节点识别方法。

Power System Critical Node Identification Based on Subnetwork Partition

With power grid topology and power flow tracing technology, a method for identifying key nodes of a power grid based on subnet division is proposed. Firstly, generator nodes are divided into subsets according to their neighborhood information and power. Then, with power flow tracking technology, a coefficient distribution matrix of the power grid is obtained. Next, a load node is divided into a generator node subset which offer the maximum power according to the coefficient distribution matrix. A multi-attribute decision-making method is used to sort the nodes in each subnet. The structure coefficient of subnet is further improved and calculated an index for measuring importance of the subnet. According to the importance of subnets, a specific proportion of candidate key nodes are extracted from each subnet. These candidate nodes are reordered with multi-attribute decision-making method to obtain the final ranking of the key nodes. Taking IEEE14, IEEE57 and IEEE118-node systems as examples, subnet division results and ranking results of important nodes of standard networks are obtained. Our method, PageRank method and multi-attribute decision-making method are used to sort key nodes, respectively. Cascade fault performance experiment and network efficiency performance are carried out on the key nodes with top ranking. It shows that the key nodes selected by the proposed algorithm have the greatest impact on propagation performance of the entire network.