Steady State Contingency analysis of electrical networks using machine learning techniques

Steady state contingency analysis aims at the assessment of the risk certain contingencies may pose to an electrical network. This is a particularly important task of network operators, especially as network stability issues become of prime importance in the current era of electricity deregulation. The article focuses on the analysis of experimental data that are produced through operating point simulation, contingency application, machinelearning cross validation (based on pre-contingency network index selection algorithms) to point out the “nature” of given contingencies. Experimental statistical results of contingency prediction and selected network state indicators are translated to electric network data in an effort to further interpret the “nature” of each contingency and produce effective predicting algorithms that support operators.