Consensus Clustering for Bi-objective Power Network Partition

Yi Wang

doi:10.17775/CSEEJPES.2020.06390

Consensus Clustering for Bi-objective Power Network Partition

2021-01-01

5

OA

AI

DOI：10.17775/CSEEJPES.2020.06390

Original

Original request for help

Save

Abstract

En 中文

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:

Consensus clustering

network partition

bi-objective partition

machine learning

Consensus Clustering for Bi-objective Power Network Partition

Abstract

Journal

Organization