Utilization of support vector machine classifiers to power system topology verification

Creating a correct power network connectivity model is very important stage of power system real-time modeling. Errors in this model may seriously degrade credibility of results of power system applications and implicate improper and potentially danger control actions. In this situation, checking correctness of power network topology model is of great importance. The aim of the paper is to present an original method for power system topology verification, using the knowledge resulting from relationships describing power system and Support Vector Machine classification technique. In the paper, theoretical background and principle of the proposed method are described. A case study shows utilization of the method for the verification of a topology model of the IEEE 14-bus test system. At the end of the paper, features of the described method are discussed.

[1]  A. M. Leite da Silva,et al.  Online topology determination and bad data suppression in power system operation using artificial neural networks , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[2]  K. Clements,et al.  Detection and identification of topology errors in electric power systems , 1988 .

[3]  Fernando L. Alvarado,et al.  Network topology determination using least absolute value state estimation , 1995 .

[4]  O. Alsaç,et al.  Generalized state estimation , 1998 .

[5]  Ali Abur,et al.  Identifying the unknown circuit breaker statuses in power networks , 1995 .

[6]  P. Bonanomi,et al.  Power System Data Validation and State Calculation by Network Search Techniques , 1983, IEEE Transactions on Power Apparatus and Systems.

[7]  V. H. Quintana,et al.  Pattern analysis in power system state estimation , 1995 .

[8]  Felix F. Wu,et al.  Detection of Topology Errors by State Estimation , 1989, IEEE Power Engineering Review.

[9]  M. Delimar,et al.  Artificial neural networks in power system topology recognition , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[10]  K. A. Clements,et al.  Topology error identification using normalized Lagrange multipliers , 1998 .

[11]  P. Panciatici,et al.  Estimation of the topological state of power system networks via interval analysis , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[12]  S. C. Srivastava,et al.  POWER SYSTEM NETWORK TOPOLOGY PROCESSING BASED ON ARTIFICIAL NEURAL NETWORKS , 1998 .

[13]  Kazimierz Wilkosz,et al.  Method for Power System Topology Verification with Use of Radial Basis Function Networks , 2007, IWANN.

[14]  Lamine Mili,et al.  A robust estimation method for topology error identification , 1999 .

[15]  Francisco García-Lagos,et al.  Modular power system topology assessment using Gaussian potential functions , 2003 .

[16]  Antonio Gomez-Exposito,et al.  Implicitly constrained substation model for state estimation , 2002 .

[17]  H. Glavitsch,et al.  Detection and identification of topological errors in online power system analysis , 1991 .

[18]  A. de la Villa Jaen,et al.  Implicitly Constrained Substation Model for State Estimation , 2002, IEEE Power Engineering Review.

[19]  A. Monticelli.  Modeling circuit breakers in weighted least squares state estimation , 1993 .

[20]  Elizete M. Lourenco,et al.  GEOMETRICALLY-BASED HYPOTHESIS TESTING FOR TOPOLOGY ERROR IDENTIFICATION , 2002 .

[21]  I. S. Costa,et al.  Identification of topology errors in power system state estimation , 1993 .