A new controlled islanding algorithm based on spectral clustering

When islanding operation of system is unavoidable, controlled islanding need determine proper splitting strategies and split the entire interconnected transmission network into islands ensuring generation/load balance and generator coherency. For a large-scale power system, the controlled islanding problem is very complex in general because a combinatorial explosion of strategy space happens. This paper mainly studies how to find proper splitting strategies of large-scale power systems using a two-step spectral clustering based method. In the first step, the machine nodes (generators and dynamic loads) will be grouped by normalized spectral clustering with dynamic models to satisfy the dynamic constraint, and the machine grouping results will be served as prior knowledge of the next step. In the second step, all the nodes will be grouped by constrained spectral clustering with power flow data to satisfy the static constraint and get the minimal cut set solution. Simulation results on IEEE 9- and 118-bus networks show that this method is efficient for controlled islanding of large-scale power systems.

[1]  Hao Li,et al.  Strategic Power Infrastructure Defense , 2005, Proceedings of the IEEE.

[2]  Wenxin Liu,et al.  Slow Coherency and Angle Modulated Particle Swarm Optimization Based Islanding of Large Scale Power Systems , 2007, 2007 International Joint Conference on Neural Networks.

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

[4]  Dan Klein,et al.  Spectral Learning , 2003, IJCAI.

[5]  Johan A. K. Suykens,et al.  Learning from General Label Constraints , 2004, SSPR/SPR.

[6]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Kai Sun,et al.  A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability , 2005 .

[8]  Kai Sun,et al.  A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability , 2005, IEEE Transactions on Power Systems.

[9]  Joe H. Chow,et al.  Time-Scale Modeling of Dynamic Networks with Applications to Power Systems , 1983 .

[10]  Xiaoming Wang,et al.  Slow coherency grouping based islanding using minimal cutsets and generator coherency index tracing using the continuation method , 2005 .

[11]  Ali Peiravi,et al.  A fast algorithm for intentional islanding of power systems using the multilevel kernel k-means approach , 2009 .

[12]  Kai Sun,et al.  Splitting strategies for islanding operation of large-scale power systems using OBDD-based methods , 2003 .

[13]  Xiaoou Tang,et al.  Constrained clustering via spectral regularization , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[15]  Ali Peiravi,et al.  Comparison of Computational Requirements of Spectral and Kernel k-means Bisectioning of Power systems , 2009 .

[16]  Zhenguo Li,et al.  Constrained clustering via spectral regularization , 2009, CVPR.

[17]  B. Lesieutre,et al.  Studies in Network Partitioning Based on Topological Structure , 2003 .