Controlled islanding of power networks based on graph reduction and spectral clustering

Intentional controlled islanding aims to split the power system into self-sustainable islands after a severe disturbance, but prior the uncontrolled network separation. Given its nature (i.e. last resort for blackout prevention), this emergency control technique must be adopted as quickly as possible. This paper proposes a computationally efficient method based on graph reduction and spectral clustering. The paper contributes by describing important details of the graph reduction process in the context of controlled islanding and by the formalisation of this process. Furthermore, it demonstrates how to adopt embedded graphs to enhance the Multiway Spectral Clustering graph partitioning. Finally, it is shown how to explicitly incorporate important cannot-link constrains between coherent generator groups into the islanding problem. The proposed method is detailed using the IEEE 39-bus test case. To evaluate the algorithm performance, the method is applied to realistically-sized PEGASE test networks

[1]  Rubén J. Sánchez-García,et al.  Hierarchical Spectral Clustering of Power Grids , 2014, IEEE Transactions on Power Systems.

[2]  Kai Sun,et al.  A study of system splitting strategies for island operation of power system: a two-phase method based on OBDDs , 2003 .

[3]  Vijay Vittal,et al.  Slow Coherency Based Cutset Determination Algorithm for Large Power Systems , 2010, IEEE Transactions on Power Systems.

[4]  G.T. Heydt,et al.  A Novel Slow Coherency Based Graph Theoretic Islanding Strategy , 2007, 2007 IEEE Power Engineering Society General Meeting.

[5]  Vladimir Terzija,et al.  Constrained spectral clustering-based methodology for intentional controlled islanding of large-scale power systems , 2015 .

[6]  Vladimir Terzija,et al.  On implementing a spectral clustering controlled islanding algorithm in real power systems , 2013, 2013 IEEE Grenoble Conference.

[7]  V. Vittal,et al.  Slow coherency-based islanding , 2004, IEEE Transactions on Power Systems.

[8]  Matthias Hein,et al.  Constrained 1-Spectral Clustering , 2012, AISTATS.

[9]  Juan Li,et al.  Controlled Partitioning of a Power Network Considering Real and Reactive Power Balance , 2010, IEEE Transactions on Smart Grid.

[10]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[11]  Joe H. Chow,et al.  A toolbox for power system dynamics and control engineering education and research , 1992 .

[12]  L. Wehenkel,et al.  Contingency Ranking With Respect to Overloads in Very Large Power Systems Taking Into Account Uncertainty, Preventive, and Corrective Actions , 2013, IEEE Transactions on Power Systems.

[13]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[14]  Lei Ding,et al.  Two-Step Spectral Clustering Controlled Islanding Algorithm , 2013, IEEE Transactions on Power Systems.

[15]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.