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

• Published in: IEEE transactions on power systems Vol. 16; no. 4; pp. 757 - 763
• Main Authors: Jensen, C.A., El-Sharkawi, M.A., Marks, R.J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2001; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Assessments; Data security; Discrimination; Feature extraction; Helium; Integrated circuit interconnections; Intelligent networks; Intelligent systems; Neural networks; Power system interconnection; Power system security; Propagation losses; Security; Training
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/59.962423

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.