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CLC number: TP39; TM74

On-line Access: 2019-07-08

Received: 2018-03-08

Revision Accepted: 2018-06-17

Crosschecked: 2019-06-11

Cited: 0

Clicked: 1166

Citations:  Bibtex RefMan EndNote GB/T7714


Hui-fang Wang


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


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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publisher="Zhejiang University Press & Springer",

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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800146

T1 - An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression
A1 - Hui-fang Wang
A1 - Chen-yu Zhang
A1 - Dong-yang Lin
A1 - Ben-teng He
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800146

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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