对电力系统中重要节点进行有效区分,有助于在资源有限的条件下对重要节点施加额外保护或改变拓扑结构,从而提高系统鲁棒性、降低事故发生的概率。受网页排序算法启发,提出电气链接结构分析的随机方法(electrical stochastic approach fo...对电力系统中重要节点进行有效区分,有助于在资源有限的条件下对重要节点施加额外保护或改变拓扑结构,从而提高系统鲁棒性、降低事故发生的概率。受网页排序算法启发,提出电气链接结构分析的随机方法(electrical stochastic approach for link structure analysis,E-SALSA)用于电力系统重要节点评估。该算法综合考虑了电力系统拓扑结构、潮流等因素对节点的影响,能够有效反映电力系统的真实情况,并且其特点更符合电力系统背景。在IEEE300节点电力系统中,使用失负荷规模和最大子群规模两个指标对E-SALSA算法与电气介数算法、基于共同引用的超链接引导的主题搜索(model based on co-citation hypertext induced topic search,MBCC-HITS)算法进行了对比分析。结果证明E-SALSA算法相比电气介数算法在两个指标上都具有优势,相比MBCC-HITS算法能够更综合考虑各方面因素对节点的影响,进而证明了E-SALSA算法的合理性、有效性。展开更多
The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in var...The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.展开更多
文摘对电力系统中重要节点进行有效区分,有助于在资源有限的条件下对重要节点施加额外保护或改变拓扑结构,从而提高系统鲁棒性、降低事故发生的概率。受网页排序算法启发,提出电气链接结构分析的随机方法(electrical stochastic approach for link structure analysis,E-SALSA)用于电力系统重要节点评估。该算法综合考虑了电力系统拓扑结构、潮流等因素对节点的影响,能够有效反映电力系统的真实情况,并且其特点更符合电力系统背景。在IEEE300节点电力系统中,使用失负荷规模和最大子群规模两个指标对E-SALSA算法与电气介数算法、基于共同引用的超链接引导的主题搜索(model based on co-citation hypertext induced topic search,MBCC-HITS)算法进行了对比分析。结果证明E-SALSA算法相比电气介数算法在两个指标上都具有优势,相比MBCC-HITS算法能够更综合考虑各方面因素对节点的影响,进而证明了E-SALSA算法的合理性、有效性。
文摘The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.
