Use of Karhunen-Loe´ve expansion in training neural networks for static security assessment

A neural network (NN) for static security assessment (SSA) of a large scale power system is proposed. A group of multi-layer perceptron type NN´s are trained to classify the security status of the power system for specific contingencies based on the pre-contingency system variables. Curse of dimensionality of the input data is reduced by partitioning the problem into smaller sub-problems. Better class separation and further dimensionality reduction is obtained by a feature selection scheme based on Karhunen-Loe´ve expansion. When each trained NN is queried on-line, it can provide the power system operator with the security status of the current operating point for a specified contingency. The parallel network architecture and the adaptive capability of the NN´s are combined to achieve high speeds of execution and good classification accuracy. With the expected emergence of affordable NN hardware, this technique has the potential to become a viable alternative to existing computationally intensive schemes for SSA