用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法

王慧芳; 张晨宇; 林冬阳; 何奔腾

doi:10.1631/FITEE.1800146

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用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法

常规文章 | Updated：2022-05-19

- 用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法
- An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression
- 信息与电子工程前沿（英文） 2019年20卷第6期页码：816-828
- Affiliations：
  
  Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  State Grid Jiangsu Electric Power Engineering Consulting Co., Ltd., Nanjing 210008, China
- Author bio：
  
  Hui-fang WANG, E-mail: huifangwang@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1800146
  中图分类号： TP39; TM74
- 收稿：2018-03-08，
  
  修回：2019-;6-11，
  
  纸质出版：2019-06
- Accepted：
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王慧芳, 张晨宇, 林冬阳, 等. 用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法[J]. 信息与电子工程前沿（英文）, 2019,20(6):816-828.

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, et al. An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 816-828.
王慧芳, 张晨宇, 林冬阳, 等. 用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法[J]. 信息与电子工程前沿（英文）, 2019,20(6):816-828. DOI： 10.1631/FITEE.1800146.

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, et al. An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 816-828. DOI： 10.1631/FITEE.1800146.

摘要

重要节点识别对电网安全意义重大。但电网在规模、结构等方面差异较大，评价指标难以涵盖电网不同状态下所有信息，因此基于指标构建的传统评估方法，其效果视情况而定，通用性不足。由此，本文提出基于人工智能的电网节点重要度评估法。首先利用网络嵌入，提出综合考虑电网结构与电气量的电网节点特征选择法。然后对具体电网，进行各类运行方式下的稳态与节点故障暂态仿真，构建能反映节点特征与节点重要度内在关系的样本集。最后，根据优化后的样本集训练支持向量回归模型，模型成熟后可用于电网节点重要度在线评估。结果表明，本方法能根据从样本中学到的信息有效评估电网节点重要度。相比传统指标构建法，本方法规避了片面性和主观性。此外，基于该人工智能框架，本方法可针对每个具体电网建立特定样本集，具有通用性。

Abstract

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.

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Keywords

references

R Albert , , , I Albert , , , GL Nakarado . . Structural vulnerability of the North American power grid . . Phys Rev E , , 2004 . . 69 ( ( 2 ): ): 025103(R) DOI: 10.1103/PhysRevE.69.025103 http://doi.org/10.1103/PhysRevE.69.025103 . .

S Angra , , , S Ahuja . . Machine learning and its applications: a review . . Int Conf on Big Data Analytics and Computational Intelligence , , 2017 . . p.57 - - 60 . . DOI: 10.1109/ICBDACI.2017.8070809 http://doi.org/10.1109/ICBDACI.2017.8070809 . .

S Arianos , , , E Bompard , , , Carbone , , , 等 . . Power grid vulnerability: a complex network approach . . Chaos , , 2009 . . 19 ( ( 1 ): ): 013119 DOI: 10.1063/1.3077229 http://doi.org/10.1063/1.3077229 . .

JL Bai , , , TQ Liu , , , GY Cao , , , 等 . . A survey on vulnerability assessment method for power system . . Power Syst Technol , , 2008 . . 32 ( ( S2 ): ): 26 - - 30 . . DOI: 10.13335/j.1000-3673.pst.2008.s2.039 http://doi.org/10.13335/j.1000-3673.pst.2008.s2.039 . .

D Basak , , , S Pal , , , DC Patranabis . . Support vector regression . . Neur Inform Process Lett Rev , , 2007 . . 11 ( ( 10 ): ): 203 - - 224 . . . .

MW Browne . . Cross-validation methods . . J Math Psychol , , 2000 . . 44 ( ( 1 ): ): 108 - - 132 . . DOI: 10.1006/jmps.1999.1279 http://doi.org/10.1006/jmps.1999.1279 . .

ZX Cai , , , XH Wang , , , XN Ren . . A review of complex network theory and its application in power systems . . Power Syst Technol , , 2012 . . 36 ( ( 11 ): ): 114 - - 121 . . DOI: 10.13335/j.1000-3673.pst.2012.11.002 http://doi.org/10.13335/j.1000-3673.pst.2012.11.002 . .

WZ Chen , , , Y Zhang , , , XM Li . . Network representation learning . . Big Data Res , , 2015 . . 1 ( ( 3 ): ): 8 - - 22 . . . .

AML da Silva , , , JL Jardim , , , LR de Lima , , , 等 . . A method for ranking critical nodes in power networks including load uncertainties . . IEEE Trans Power Syst , , 2016 . . 31 ( ( 2 ): ): 1341 - - 1349 . . DOI: 10.1109/TPWRS.2015.2413847 http://doi.org/10.1109/TPWRS.2015.2413847 . .

WL Fan , , , H Ping , , , ZG Liu . . Multi-attribute node importance evaluation method based on Gini-coefficient in complex power grids . . IET Gener Transm Distrib , , 2016 . . 10 ( ( 9 ): ): 2027 - - 2034 . . DOI: 10.1049/iet-gtd.2015.0803 http://doi.org/10.1049/iet-gtd.2015.0803 . .

JH Friedman . . Stochastic gradient boosting . . Comput Stat Data Anal , , 2002 . . 38 ( ( 4 ): ): 367 - - 378 . . DOI: 10.1016/S0167-9473(01)00065-2 http://doi.org/10.1016/S0167-9473(01)00065-2 . .

A Grover , , , J Leskovec . . Node2vec: scalable feature learning for networks . . Proc 22 nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , , 2016 . . p.855 - - 864 . . DOI: 10.1145/2939672.2939754 http://doi.org/10.1145/2939672.2939754 . .

A Jović , , , K Brkić , , , N Bogunović . . A review of feature selection methods with applications . . Proc 38 th Int Convention on Information and Communication Technology, Electronics and Microelectronics , , 2015 . . p.1200 - - 1205 . . DOI: 10.1109/MIPRO.2015.7160458 http://doi.org/10.1109/MIPRO.2015.7160458 . .

WY Ju , , , YH Li . . Identification of critical lines and nodes in power grid based on maximum flow transmission contribution degree . . Autom Electr Power Syst , , 2012 . . 36 ( ( 2 ): ): 6 - - 12 . . . .

CB Li , , , JZ Liang . . A novel method of power grid differential planning . . Autom Electr Power Syst , , 2009 . . 33 ( ( 24 ): ): 11 - - 15 . . DOI: 10.3321/j.issn:1000-1026.2009.24.003 http://doi.org/10.3321/j.issn:1000-1026.2009.24.003 . .

CB Li , , , WC Liu , , , YJ Cao , , , 等 . . Method for evaluating the importance of power grid nodes based on Pagerank algorithm . . IET Gener Transm Distrib , , 2014 . . 8 ( ( 11 ): ): 1843 - - 1847 . . DOI: 10.1049/iet-gtd.2014.0051 http://doi.org/10.1049/iet-gtd.2014.0051 . .

ZZ Lin , , , FS Wen , , , HF Wang , , , 等 . . CRITIC-based node importance evaluation in skeleton-network reconfiguration of power grids . . IEEE Trans Circ Syst Ⅱ , , 2017 . . 65 ( ( 2 ): ): 206 - - 210 . . DOI: 10.1109/TCSII.2017.2703989 http://doi.org/10.1109/TCSII.2017.2703989 . .

ABM Nasiruzzaman , , , HR Pota . . Critical node identification of smart power system using complex network framework based centrality approach . . North American Power Symp , , 2011 . . p.1 - - 6 . . DOI: 10.1109/NAPS.2011.6025194 http://doi.org/10.1109/NAPS.2011.6025194 . .

XD Pan , , , J Wu , , , DC Liu , , , 等 . . A method for constructing core backbone grid in differential planning based on importance degrees of components . . Autom Electr Power Syst , , 2014 . . 38 ( ( 19 ): ): 40 - - 46 . . DOI: 10.7500/AEPS20130522006 http://doi.org/10.7500/AEPS20130522006 . .

F Pedregosa , , , G Varoquaux , , , A Gramfort , , , 等 . . Scikit- Learn: machine learning in Python . . J Mach Learn Res , , 2011 . . 12 2825 - - 2830 . . . .

B Perozzi , , , R Al-Rfou , , , S Skiena . . DeepWalk: online learning of social representations . . Proc 20 th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , , 2014 . . p.701 - - 710 . . DOI: 10.1145/2623330.2623732 http://doi.org/10.1145/2623330.2623732 . .

S M Ross . . Stochastic Processes . . Wiley, New York, SUA , , 1983 . . .

YD Tan , , , XR Li , , , Y Cai , , , 等 . . Critical node identification for complex power grid based on electrical distance . . Proc CSEE , , 2014 . . 34 ( ( 1 ): ): 146 - - 152 . . DOI: 10.13334/j.0258-8013.pcsee.2014.01.017 http://doi.org/10.13334/j.0258-8013.pcsee.2014.01.017 . .

L Vanfretti , , , F Milano . . Application of the PSAT, an open source software, for educational and research purposes . . IEEE Power Engineering Society General Meeting , , 2007 . . p.24 - - 28 . . DOI: 10.1109/PES.2007.385952 http://doi.org/10.1109/PES.2007.385952 . .

B Wang , , , BW Fang , , , YJ Wang , , , 等 . . Power system transient stability assessment based on big data and the core vector machine . . IEEE Trans Smart Grid , , 2016 . . 7 ( ( 5 ): ): 2561 - - 2570 . . DOI: 10.1109/TSG.2016.2549063 http://doi.org/10.1109/TSG.2016.2549063 . .

HF Wang , , , ZB Shan , , , GL Ying , , , 等 . . Evaluation method of node importance for power grid considering inflow and outflow power . . J Mod Power Syst Clean Energy , , 2017 . . 5 ( ( 5 ): ): 696 - - 703 . . DOI: 10.1007/s40565-016-0234-3 http://doi.org/10.1007/s40565-016-0234-3 . .

XF Wang , , , YH Song , , , M Irving . . Modern Power Systems Analysis . . Springer, Boston, USA , , 2008 . . .

L Xu , , , XL Wang , , , XF Wang . . Cascading failure mechanism in power grid based on electric betweenness and active defence . . Proc CSEE , , 2010 . . 30 ( ( 13 ): ): 61 - - 68 . . DOI: 10.13334/j.0258-8013.pcsee.2010.13.010 http://doi.org/10.13334/j.0258-8013.pcsee.2010.13.010 . .

LX Xu , , , JY Liu , , , Y Liu , , , 等 . . Node importance classified comprehensive assessment . . Proc CSEE , , 2014 . . 34 ( ( 10 ): ): 1609 - - 1617 . . DOI: 10.7500/AEPS20130522006 http://doi.org/10.7500/AEPS20130522006 . .

Y Xu , , , ZY Dong , , , K Meng , , , 等 . . Real-time transient stability assessment model using extreme learning machine . . IET Gener Transm Distrib , , 2011 . . 5 ( ( 3 ): ): 314 - - 322 . . DOI: 10.1049/iet-gtd.2010.0355 http://doi.org/10.1049/iet-gtd.2010.0355 . .

C Yang , , , ZY Liu , , , DL Zhao , , , 等 . . Network representation learning with rich text information . . Proc 24 th Int Conf on Artificial Intelligence , , 2015 . . p.2111 - - 2117 . . . .

RD Zimmerman , , , CE Murillo-Sanchez , , , RJ Thomas . . MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education . . IEEE Trans Power Syst , , 2011 . . 26 ( ( 1 ): ): 12 - - 19 . . DOI: 10.1109/TPWRS.2010.2051168 http://doi.org/10.1109/TPWRS.2010.2051168 . .

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