Neural networks applied to preventive control measures for the dynamic security of isolated power systems with renewables

Abstract

This paper presents an artificial neural network (ANN) based approach for the definition of preventive control strategies of autonomous power systems with a large renewable power penetration. For a given operating point, a fast dynamic security evaluation for a specified wind perturbation is performed using an ANN. If insecurity is detected, new alternative stable operating points are suggested, using a hybrid ANN-optimization approach that checks several feasible possibilities, resulting from changes in power produced by diesel and wind generators, and other combinations of diesel units in operation. Results obtained from computer simulations of the real power system of Lemnos (Greece) support the validity of the developed approach.