32 IEEE Computer Applications in Power ISSN 0895-0156/02/$17.00©2002 IEEE L arge interconnected power systems with dispersed and geographically isolated generators and load constitute a majority of the power network. Pre- sent-day power systems are dynamic in nature, where the network topology frequently changes with load demand. With increase in load, the power system net- work is loaded to its limits, making it susceptible to col- lapse 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 dif- ferent line-loading patterns for training, the network suc- cessfully classifies the unknown loading patterns. This powerful and versatile feature is especially useful for K.S. Swarup and P.B. Corthis are with the Indian Institute of Technology Madras, Chennai, India. ANN Approach Assesses System Security K. Shanti Swarup, P. Britto Corthis The key issues in security assessment are fast identification of insecure contingencies and evaluation of their impact on power system operation © EYEWIRE