Line Outage Security Assessment using Combinatorial Form Neural Network Topology (CFNNT)

I. Musirin and T.K.A. Rahman (Malaysia)


Line outage severity, FVSI, security assessment,neural network.


Several incidents that occurred around the world involving power failure caused by unscheduled line or generator outages were identified as one of the main contributors to power failure and cascading blackout in electric power environment. With the advancement of computer technologies, artificial intelligence (AI) has been widely accepted as one alternative method that can be applied in predicting the occurrence of unscheduled disturbance. This paper presents the application of artificial neural network (ANN) in the prediction of line outage severity from a set of pre-outage data. The outputs of the developed ANN are further separated into their respective loading condition through the loading classifier modules and finally are ranked to two categories according to their severities. The architecture of the complete system technique combines the ANN topology, loading classifier and fundamental expert system (ES) modules and termed as Combinatorial Form Neural Network Topology (CFNNT). The results from this technique can be utilized by the power system operators and planners to carry out further monitoring and planning processes in the Energy Management System (EMS).

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