Dynamic security border identification using enhanced particle swarm optimization

• Published in: IEEE transactions on power systems Vol. 17; no. 3; pp. 723 - 729
• Main Authors: Kassabalidis, I.N., El-Sharkawi, M.A., Marks, R.J., Moulin, L.S., Alves da Silva, A.P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2002; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Borders; Computational modeling; Computer security; Data security; Deregulation; Dynamical systems; Dynamics; Neural networks; Nonlinear dynamics; Optimization; Particle swarm optimization; Power system dynamics; Power system reliability; Power system security; Power system simulation; Security; Space technology; Tracking
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2002.800942

The ongoing deregulation of the energy market increases the need to operate modern power systems close to the security border. This requires enhanced methods for the vulnerability border tracking. The high-dimensional nature of power systems' operating space makes this difficult. However, new multiagent search techniques such as particle swarm optimization have shown great promise in handling high-dimensional nonlinear problems. This paper investigates the use of a new variation of particle swarm optimization to identify points on the security border of the power system, thereby identifying a vulnerability margin metric for the operating point.