Loss-of-load state identification using self-organizing map

This paper presents a method for classifying power system states as loss-of-load or not using Kohonen's self-organizing map (SOM). The main feature of SOM is the ability to map input data from an n-dimensional space to a lower dimensional (usually two dimensional) space while maintaining the original topological relationships. Real and reactive power at each load bus and available real power generation at each generation bus are taken as input features. OPF calculations are performed on the weights of each neuron in the map to determine whether the neuron is representative of loss-of-load or not. The loss-of-load status of a new system state can be quickly identified by the loss-of-load status of the nearest neuron. An example illustrating the approach shows that the SOM can accurately classify the loss-of-load status of power system states. This proposed method is useful for power system operation, power system reliability assessment and state screening.