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一种基于平均交互作用指标的多馈入直流系统落点选取方法 被引量:2

Method of selecting landing area of multi-infeed DC system based on average interaction index
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摘要 在多馈入直流系统中,密集的直流线路间交互影响作用给系统带来了不可忽略的影响。本文首先根据多馈入交互影响因子定义了节点间的平均交互作用指标,并使用聚类手段对平均交互作用矩阵中元素对象进行分类,其目的是使交互作用强的节点组成一类,不同类间节点交互作用弱;然后每条直流选择一个类作为备选落点区域,直接剔除掉那些交互作用强的节点组合,所有的方案构成方案集合后进行综合评估;最后以IEEE-RTS24节点系统为例进行落点选择,分析与对比利用本文提出的方法流程对受端系统预先分区后再评估结果与传统方法的评估筛选结果。计算结果表明,文中提出的平均交互作用指标有一定的合理性,可以当做初选备选区域的选择依据,系统经过分区再进行落点选择筛选出的最优方案与传统方法结果一致,降低了计算规模,提高了筛选效率,有助于实际工程应用。 In muhi-infeed DC system, the dense DC lines bring impact to the system which is not to be ignored. Based on the muhi-infeed interaction factor, this paper defined average interaction index of nodes and used cluste- ring method to classify the elements of interaction matrix. The aim was to make the nodes with strong interaction to form a class and the interaction between different classes to be weak. Then each DC line selected one class as its alternative landing area and in this way the scheme of nodes with strong interaction was eliminated directly. Com- prehensive evaluation was carried out after all the schemes was setting together. Finally, IEEE-RTS24 bus system is taken as an example to select DC placement. The evaluating results of traditional method and the method proposed in this paper were analyzed and compared. The calculation results showed that the proposed average interaction in- dex was rational in certain degree and could be used as basis for alternative region selection and the optimal scheme by system partition method was consistent with traditional one. The calculation scale is reduced and the screening efficiency is improved and is helpful to the practical engineering application.
作者 蔡国伟 史一明 杨德友 CAI Guo-wei SHI Yi-ming YANG De-you(School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China)
出处 《电工电能新技术》 CSCD 北大核心 2017年第3期1-7,共7页 Advanced Technology of Electrical Engineering and Energy
基金 国家自然科学基金项目(51507028) 国家电网公司科技项目(XT71-15-053)
关键词 多馈入直流系统 平均交互作用 聚类 筛选 最优 multi-infeed DC system average interaction clustering screening optimal
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