Power system security assessment using neural networks: Feature selection using Fisher discrimination

One of the most Important considerations in applying neural networks to pow er system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces o f equipment each of which may have an affect on the security of the system. Neural networks have shown great promise for their ability to quickly and accurately predict the system security when trained with data collected fro m a small subset of system variables. This paper investigates the use of Fi sher's linear discriminant function, coupled with feature selection techniq ues as a means for selecting neural network training features for power sys tem security assessment. A case study is performed on the IEEE 50-generator system to illustrate the effectiveness of the proposed techniques.