Partitioning of electric networks into zones or areas is a procedure that has numerous applications in power system planning, operation and control. Spectral clustering based approaches are among the most favoured ones to solve the partitioning problem. Applications of spectral clustering include definition of control zones, analysis of connectivity structure of power networks, intentional controlled islanding, design of sectionalising strategies, and visualisation. Although spectral clustering is a state-of-the-art family of methods with numerous extensions, some practical issues can arise when applying it to large-scale power networks. While spectral clustering becomes significantly more robust to outliers when combined with a robust post-processing method like k-medoids, the connectedness of the resulting partitioning cannot be guaranteed. This paper proposes a greedy algorithm to solve the connectedness issues inherent to many robust post-processing methods. Furthermore, it is proposed to utilise a label propagation based heuristic to improve the quality of the final partitions. The test results evaluate the steps of the methodology on a large-scale 1354-bus PEGASE test network.

Original languageEnglish
Title of host publicationConference Proceedings - 17th IEEE International Conference on Smart Technologies, EUROCON 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages805-809
Number of pages5
ISBN (Electronic)978-1-5090-3843-5
DOIs
Publication statusPublished - 2017
EventIEEE EUROCON 2017: 17th IEEE International Conference on Smart Technologies - Ohrid, Macedonia, The Former Yugoslav Republic of
Duration: 6 Jul 20178 Jul 2017
Conference number: 17
http://eurocon2017.org/

Conference

ConferenceIEEE EUROCON 2017
CountryMacedonia, The Former Yugoslav Republic of
CityOhrid
Period6/07/178/07/17
Internet address

    Research areas

  • cluster connectedness, cluster refinement, power network clustering, spectral clustering

ID: 36048608