Combined use of unsupervised and supervised learning for large scale power system static security mapping

This paper presents an artificial neural-net based technique which combines supervised and unsupervised learning for evaluating on-line power system static security. It automatically scans contingencies of a power system. The proposed approach allows the on-line security evaluation of (N -1) contingencies by considering the pre-fault state vector. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping (GHSOM) in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 14 buses power system are presented and discussed. The analysis using such method provides accurate results with a great saving in computation time.