Fast Search Algorithm for Key Transmission Sections Based on Topology Converging Adjacency Matrix

The traditional method of searching transmission section generally has the problem of omission and time-consuming. A topology converging algorithm of power grid adjacency matrix is proposed in this paper. It aggregates neighboring buses based on a simple matrix transformation rule, and identify key transmission sections based on matrix operations and power flow distribution factors. This method has the advantage of no presetting the sub-zone, which can avoid the simplification of the grid topology in the sub-zone and improve the calculation accuracy. The complex logical operations of the traversal algorithm adopted by most of traditional methods can be avoided, and the calculation speed can be effectively improved. Besides, the algorithm also has a memory characteristic which can be applied to the online search of key transmission sections. The results of several IEEE test systems show the effectiveness of the proposed algorithm.

[1]  Ju Wenyun,et al.  Key Transmission Sections Analysis Based on Complex Network Theory , 2013 .

[2]  Zhen Xufeng,et al.  New Algorithm for Searching Tie Lines Based on Deviation Path , 2012 .

[3]  J. Ren,et al.  A Fast Search Algorithm for Transmission Section Based on K Shortest Paths , 2014 .

[4]  Chunyan Li,et al.  Analysis of the blackout in Europe on November 4, 2006 , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[5]  A. Abur,et al.  Total transfer capability computation for multi-area power systems , 2006, IEEE Transactions on Power Systems.

[6]  Zhao Yishu Fast Search for Transmission Section Based on Graph Theory , 2006 .

[7]  A. Conejo,et al.  Multi-Area Energy and Reserve Dispatch Under Wind Uncertainty and Equipment Failures , 2013, IEEE Transactions on Power Systems.

[8]  Lingfeng Wang,et al.  Day-Ahead Scheduling of Power System Incorporating Network Topology Optimization and Dynamic Thermal Rating , 2019, IEEE Access.

[9]  Xiaochen Zhang,et al.  Decentralized Total Transfer Capability Evaluation Using Domain Decomposition Methods , 2016, IEEE Transactions on Power Systems.

[10]  Luo Gang,et al.  Automatic identification of transmission sections based on complex network theory , 2014 .

[11]  Lars Nordström,et al.  Distributed Topology Inference for Electric Power Grids , 2017, IEEE Transactions on Industrial Informatics.

[12]  F. Gubina,et al.  Multiple regression models as identifiers of power system weak points , 2006 .

[13]  Bo Zhi-qian,et al.  Fast Search for Key Transmission Section Based on Power Component of Line , 2010 .

[14]  Yoshihiko Susuki,et al.  Nonlinear Koopman modes and power system stability assessment without models , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[15]  Wei Zhang,et al.  An Automatic Shedding Decision System for the Backup Protection of a Transmission Network , 2006, 2006 International Conference on Power System Technology.

[16]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  G. C. Ejebe,et al.  Fast calculation of linear available transfer capability , 1999 .

[18]  V. Madani,et al.  Shedding light on blackouts , 2004, IEEE Power and Energy Magazine.

[19]  Zhang Boming,et al.  Electrical Zone Division Based Automatic Discovery of Flowgates , 2011 .

[20]  Jiandong Duan,et al.  Typical transmission section searching method considering geographical attributes for large power grids , 2019 .

[21]  Peng Wang,et al.  Determination of optimal total transfer capability using a probabilistic approach , 2006, IEEE Transactions on Power Systems.

[22]  Faruk Kazi,et al.  Cascading Failure Analysis for Indian Power Grid , 2016, IEEE Transactions on Smart Grid.