Title
Slow Coherency and Angle Modulated Particle Swarm Optimization Based Islanding of Large Scale Power Systems
Abstract
Power system islanding is an efficient way to avoid catastrophic wide area blackouts, such as the 2003 North American blackout. Islanding of large-scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution within reasonable time using analytical methods. This paper presents a new method to solve this problem. In the proposed method, angle modulated particle swarm optimization (AMPSO) is utilized to find a number of efficient islanding solutions for large-scale power systems due to its computational efficiency. First, desired generators groups is obtained using slow coherency algorithm. AMPSO is then used to optimize a fitness function defined according to both generation/load balance and similarity to the desired generator grouping. In doing so, the resulted islanding solutions provide good static and dynamic stability. Simulations for power systems of different scales demonstrate the effectiveness of the proposed algorithm.
Year
DOI
Venue
2009
10.1109/IJCNN.2007.4371280
Advanced Engineering Informatics
Keywords
Field
DocType
large scale power systems,pwer system islanding,slow coherency algorithm,power system,power system dynamic stability,slow coherency,load balance,power system islanding,particle swarm optimisation,generator group,combinatorial mathematics,load management,combinatorial explosion problem,large-scale power system,analytical method,splitting strategies,islanding solution,north american blackout,catastrophic wide area blackouts,proposed algorithm,distributed power generation,dynamic stability,angle modulated particle swarm,generator grouping,fitness function optimization,static stability,new method,particle swarm optimization,angle modulated particle swarm optimization,fitness function
Computer science,Control theory,Artificial intelligence,Combinatorial explosion,Islanding,Particle swarm optimization,Load management,Mathematical optimization,Pattern recognition,Load balancing (computing),Electric power system,Fitness function,Blackout
Journal
Volume
Issue
ISSN
23
1
1098-7576 E-ISBN : 978-1-4244-1380-5
ISBN
Citations 
PageRank 
978-1-4244-1380-5
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Li Liu1111.80
Wenxin Liu2968.39
David A. Cartes36411.09
Il-Yop Chung4131.95