Unsupervised Neural Net Classification of Power System Static Security States

The authors study the application of Kohonen's self-organizing feature map to power system static security assessment. The Kohonen classifier maps vectors of an N-dimensional space to a two-dimensional neural net in a nonlinear way, preserving the topological order of the vectors which, in general, is not known a priori. The classification of line-loading patterns by the Kohonen network is demonstrated for two different test systems. The generalization capability of the Kohonen network permits the correct classification of system states which have not been encountered during the training phase. This feature is extremely important for power system operation where it is unrealistic to expect that all possible cases will be encountered during off-line simulation

[1]  H. H. Yan,et al.  An improved Hopfield model for power system contingency classification , 1990, IEEE International Symposium on Circuits and Systems.

[2]  Se-young Oh.  A Pattern Recognition and Associative Memory Approach to Power System Security Assessment , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  R. Fischl,et al.  The application of decision theory to contingency selection , 1982 .

[4]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[5]  Dejan J. Sobajic,et al.  An artificial intelligence system for power system contingency screening , 1988 .

[6]  R.J. Marks,et al.  Artificial neural networks for power system static security assessment , 1989, IEEE International Symposium on Circuits and Systems,.

[7]  D. Niebur,et al.  Power system static security assessment using the Kohonen neural network classifier , 1991 .

[8]  S. Kumagai,et al.  Steady-State Security Regions of Power Systems , 1982 .

[9]  G. Ejebe,et al.  Automatic Contingency Selection , 1979, IEEE Transactions on Power Apparatus and Systems.

[10]  B. Stott,et al.  Review of load-flow calculation methods , 1974 .

[11]  G. W. Stagg,et al.  Computer methods in power system analysis , 1968 .

[12]  Michel Declercq,et al.  Implementation of a neuron dedicated to Kohonen maps with learning capabilities , 1990, IEEE Proceedings of the Custom Integrated Circuits Conference.

[13]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[14]  A. J. Germond,et al.  Impulse Test Fault Diagnosis on Power Transformers Using Kohonen"s Self-Organizing Neural Network , 1991 .

[15]  Antti J. Koivo,et al.  Security Evaluation in Power Systems Using Pattern Recognition , 1974 .

[16]  Hiroyuki Mori,et al.  An artificial neural-net based technique for power system dynamic stability with the Kohonen model , 1991 .