Optimal Fuzzy Self-Organizing Structure for Voltage Security Margin Estimation

In rece nt years, research efforts have been focused to estimat ing voltage security margins.voltage security margin shows how close the current operating of a power system is to a voltagecollapse point. one main disadvantage of these techniques is that they require a large amount ofcomputations; therefo re they are not efficient for on-line use in power control centers. thereforethe inte llige nt networks classification techniques for systems that are illustrated by series of dataare offered. in this paper, two methods are used to estimate voltage security margin. the firstmethod is general fuzzy min-max neural network (gfmm nns.) with on-line adaptation. thesecond method which we named it as fuzzy self-organizing network, combines twostructures of kohonen and gfmm neural network then by using akaike criterion the optimalvalues of the proposed network parameters are dete rmined. the data set needed for training thisstructure is obtained from the minimum singular valu e of the power flow jacobian matrix.these methods are applied on the ieee 30-bus system with 2000 simulated data randomlygenerated from different operating conditions. finally, the results compared with the three -layerfeed forward neur al network as the most common used neural networks. the results clearlyshow the advantage and high efficiency of the proposed structure.