Constrained spectral clustering‐based methodology for intentional controlled islanding of large‐scale power systems. Issue 1 (1st January 2015)

• Record Type: Journal Article
• Authors: Quirós‐Tortós, Jairo; Sánchez‐García, Rubén; Brodzki, Jacek; Bialek, Janusz; Terzija, Vladimir
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 1(2015)
• Page Start: 31
• Page End: 42
• Publication Date: 2015-01-01
• Subjects: distributed power generation -- power distribution faults -- power distribution reliability -- load flow -- time‐domain analysis; constrained spectral clustering‐based methodology -- intentional controlled islanding -- large‐scale power system -- cascading outages impact -- large‐area blackout -- minimal power flow disruption -- IEEE 39‐bus test system -- IEEE 118‐bus test system -- time‐domain simulation -- realistically sized system -- Great Britain network; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2014.0228
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Intentional controlled islanding is an effective corrective approach to minimise the impact of cascading outages leading to large‐area blackouts. This study proposes a novel methodology, based on 'constrained spectral clustering', that is computationally very efficient and determines an islanding solution with minimal power flow disruption, while ensuring that each island contains only coherent generators. The proposed methodology also enables operators to constrain any branch, which must not be disconnected, to be excluded from the islanding solution. The methodology is tested using the dynamic models of the IEEE 39‐ and IEEE 118‐bus test systems. Time‐domain simulation results for different contingencies are used to demonstrate the effectiveness of the proposed methodology to minimise the impact of cascading outages leading to large‐area blackouts. In addition, a realistically sized system (a reduced model of the Great Britain network with 815 buses) is used to evaluate the efficiency and accuracy of the methodology in large‐scale networks. These simulations demonstrate that the author's methodology is more efficient, in a factor of approximately 10, and more accurate than another existing approach for minimal power flow disruption.