Academic
Publications
Power system security assessment using neural networks: feature selection using Fisher discrimination

Power system security assessment using neural networks: feature selection using Fisher discrimination,10.1109/59.962423,IEEE Transactions on Power Sys

Power system security assessment using neural networks: feature selection using Fisher discrimination   (Citations: 57)
BibTex | RIS | RefWorks Download
One of the most important considerations in applying neural networks to power system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an effect 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 from a small subset of system variables. This paper investigates the use of Fisher's linear discriminant function, coupled with feature selection techniques as a means for selecting neural network training features for power system security assessment. A case study is performed on the IEEE 50-generator system to illustrate the effectiveness of the proposed techniques
Journal: IEEE Transactions on Power Systems - IEEE TRANS POWER SYST , vol. 16, no. 4, pp. 757-763, 2001
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: