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CSEE Journal of Power and Energy Systems 2022, 8(4): 973-982 https://doi.org/10.17775/CSEEJPES.2020.06390
Open Access | Issue | Published: 10 September 2021

Consensus Clustering for Bi-objective Power Network Partition

Show Author's Information Yi Wang,  Luzian Lebovitz,  Kedi Zheng,  Yao Zhou( )
Power Systems Laboratory, ETH Zurich, 8092 Zurich, Switzerland
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
School of Engineering, the University of Edinburgh, EH9 3FB, Edinburgh, UK
Keywords:
machine learning, Consensus clustering, network partition, bi-objective partition
Cite this article:
Wang Y, Lebovitz L, Zheng K, et al. Consensus Clustering for Bi-objective Power Network Partition. CSEE Journal of Power and Energy Systems, 2022, 8(4): 973-982. https://doi.org/10.17775/CSEEJPES.2020.06390
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Abstract Full text About this article

Partitioning a complex power network into a number of sub-zones can help realize a "divide-and-conquer" management structure for the whole system, such as voltage and reactive power control, coherency identification, power system restoration, etc. Extensive partitioning methods have been proposed by defining various distances, applying different clustering methods, or formulating varying optimization models for one specific objective. However, a power network partition may serve two or more objectives, where a trade-off among these objectives is required. This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition. By varying the weights of different partitions for different objectives, Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods. Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.


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About this article

Consensus Clustering for Bi-objective Power Network Partition

Show Author's information Yi Wang, Luzian Lebovitz, Kedi Zheng, Yao Zhou( )
Power Systems Laboratory, ETH Zurich, 8092 Zurich, Switzerland
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
School of Engineering, the University of Edinburgh, EH9 3FB, Edinburgh, UK

Abstract

Partitioning a complex power network into a number of sub-zones can help realize a "divide-and-conquer" management structure for the whole system, such as voltage and reactive power control, coherency identification, power system restoration, etc. Extensive partitioning methods have been proposed by defining various distances, applying different clustering methods, or formulating varying optimization models for one specific objective. However, a power network partition may serve two or more objectives, where a trade-off among these objectives is required. This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition. By varying the weights of different partitions for different objectives, Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods. Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.

Keywords: machine learning, Consensus clustering, network partition, bi-objective partition

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Received: 29 November 2020
Revised: 15 February 2021
Accepted: 04 March 2021
Published: 10 September 2021
Issue date: July 2022

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© 2020 CSEE

Acknowledgements

Acknowledgements

This work was supported in part by the National Key R&D Program of China (No. 2016YFB0900100) and the Major Smart Grid Joint Project of National Natural Science Foundation of China and State Grid (No. U1766212).

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