| 引用本文: | 耿俊琪,孙贤明,宋蕙慧,等.受互联网思路启发的电力系统重要节点评估算法.[J].国防科技大学学报,2023,45(3):211-218.[点击复制] |
| GENG Junqi,SUN Xianming,SONG Huihui,et al.Important node evaluation algorithm for electrical power system inspired by internet thinking[J].Journal of National University of Defense Technology,2023,45(3):211-218[点击复制] |
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| 受互联网思路启发的电力系统重要节点评估算法 |
| 耿俊琪1,2,孙贤明1,宋蕙慧3,曲延滨3 |
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(1. 山东理工大学 电气与电子工程学院, 山东 淄博 255000;2. 国网山东省电力公司淄博供电公司, 山东 淄博 255000;3. 哈尔滨工业大学(威海) 新能源学院, 山东 威海 264200)
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| 摘要: |
| 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。 |
| 关键词: 复杂网络 电力系统 网页算法 重要节点 |
| DOI：10.11887/j.cn.202303024 |
| 投稿日期：2021-09-01 |
| 基金项目:国家自然科学基金资助项目(51907109) |
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| Important node evaluation algorithm for electrical power system inspired by internet thinking |
| GENG Junqi1,2, SUN Xianming1, SONG Huihui3, QU Yanbin3 |
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(1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;2. State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China;3. School of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China)
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| Abstract: |
| Effective identification of important nodes in power system is helpful to improve robustness of system and reduce the probability of accidents by applying additional protection or changing the topology of important nodes under limited resources. Inspired by the web page sorting algorithm, an algorithm called E-SALSA (electrical stochastic approach for link structure analysis) was proposed for evaluating important nodes in power system. Taking into account the influence of power system topology, power flow and other factors on nodes, this algorithm can effectively reflect the true situation of power system, and its features are more in line with the background of power system. In the IEEE300 node power system, the E-SALSA algorithm was compared with the electrical median algorithm and the MBCC-HITS (model based on co-citation hypertext induced topic search) algorithm by using the two indexes of the scale of load loss and the maximum subgroup size. The results show that the E-SALSA algorithm has advantages over electrical median algorithm in both indicators. Compared with MBCC-HITS algorithm, E-SALSA algorithm can use all factors more comprehensively on the impact of nodes, which further proves its rationality and effectiveness. |
| Keywords: complex network electrical power system webpage algorithm important nodes |
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