An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

Hui-fang WANG; Chen-yu ZHANG; Dong-yang LIN; Ben-teng HE

doi:10.1631/FITEE.1800146

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An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

Regular Papers | Updated：2022-05-19

- An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 6, Pages: 816-828(2019)
- 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
  CLC： TP39; TM74
- Received：08 March 2018，
  
  Revised：2019-;6-11，
  
  Published：2019-06
- Accepted：
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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：

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

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