Supervised learning approach to online contingency screening and ranking in power systems

This paper proposes a supervised learning approach to fast and accurate power system security assessment and contingency analysis. The severity of the contingency is measured by two scalar performance indices (PIs): Voltage-reactive power Performance Index, PIVQ and line MVA Performance Index, PIMVA. In this paper, Feed-Forward Artificial Neural Network (FFNN) is employed that uses pattern recognition methodology for security assessment and contingency analysis. A feature selection technique based on the correlation coefficient has been employed to identify the inputs for the FFNN. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system at different loading conditions corresponding to single line outage. The overall accuracy of the test results for unknown patterns highlights the suitability of the approach for online applications at Energy Management Center.

► Supervised learning network for contingency screening and ranking.
► Generalized to handle new topologies and operating conditions.
► Suitable model for online applications at Energy Management Center.