Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based Community Detection Algorithm
Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-sc...
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| Published in: | IEEE transactions on power systems Vol. 37; no. 2; pp. 1612 - 1622 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0885-8950, 1558-0679 |
| Online Access: | Get full text |
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| Summary: | Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0885-8950 1558-0679 |
| DOI: | 10.1109/TPWRS.2021.3107847 |