基于改进泰尔熵和熵度的电力系统关键节点识别

为了弥补信息熵考虑因素单一的缺陷,引入泰尔熵并对其适当改进用于电力系统关键节点识别中。改进后的泰尔熵将系统线路按照负载率进行分区,并分别计算节点功率变化时同区及区间潮流分布的均匀程度,然后采用负载率对同区结果进行加权,得到节点的改进泰尔熵指标。为了使识别结果更贴近工程实际,采用熵度评估了节点网络拓扑位置的重要性,然后提出了关键节点综合评估指标。综合评估指标兼顾节点网络拓扑结构重要性和其功率变化对潮流的影响情况,通过对IEEE-39节点系统进行关键节点识别验证,验证了方法的可行性和有效性。

The identification of key nodes of power system based on improved Theil entropy and entropy degree

The Theil entropy is introduced and improved to identify key nodes of power system ,which make up for the deficiency calculating Shannon entropy using monotonous factor. The lines parted according to the load rate and then the contribution ratios of parted sections to uniform degree of power flow distributed of system are calculated, respectively. Then the load rate is used to weighted the results of the same part and the improved Theil entropy index of node is obtained. In order to make the result close to engineering practice, the entropy degree is used to evaluate the importance of position of nodes. Base on this, comprehensive evaluation index of nodes is proposed, which taking into account the importance of position of nodes in the network and the influence of power change on the power flow. The simulation of IEEE-39 node system is done, and the feasibility and validity of the proposed method are proved.