ANN approach assesses system security

Abstract

Large interconnected power systems with dispersed and geographically isolated generators and load constitute a majority of the power network. Present-day power systems are dynamic in nature, where the network topology frequently changes with load demand. With increase in load, the power system network is loaded to its limits, making it susceptible to collapse even under minor disturbances. In order to operate the power system economically, the current operating state of the system must be identified as either secure or insecure. An artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system. The work demonstrates the feasibility of classification of load patterns for power system static security assessment using a Kohonen self-organizing feature map. The most important aspect of this network is its generalization property. Using 15 different line-loading patterns for training, the network successfully classifies the unknown loading patterns. This powerful and versatile feature is especially useful for power system operation. Research is in progress to include contingency analysis in the security assessment program.