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

Abstract

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.<>