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CLC number: TP39; TM74

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2019-06-11

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hui-fang Wang

http://orcid.org/0000-0002-1483-364X

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.6 P.816-828

http://doi.org/10.1631/FITEE.1800146


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


Author(s):  Hui-fang Wang, Chen-yu Zhang, Dong-yang Lin, Ben-teng He

Affiliation(s):  Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   huifangwang@zju.edu.cn

Key Words:  Power grid, Artificial intelligence, Node importance, Text-associated DeepWalk, Network embedding, Support vector regression


Hui-fang Wang, Chen-yu Zhang, Dong-yang Lin, Ben-teng He. 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.

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year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800146"
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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.

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

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。

关键词:电网;人工智能;节点重要度;TADW法;网络嵌入;支持向量回归

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