Title
Application of metaheuristic methods to reactive power planning: a comparative study for GA, PSO and EPSO
Abstract
This paper proposes the application of metaheuristic methods to Reactive Power Planning (RPP). RPP involves optimal allocation of reactive sources to satisfy voltage constraints during normal and contingency states. The main objective of the proposed RPP is to make a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). The overall problem is formulated as a large scale mixed integer nonlinear programming problem. The proposed RPP problem is a combinatorial optimization problem, which cannot be solved easily by conventional optimization methods. Metaheuristic methods are reported to be efficient to solve combinatorial optimization problems. Among the well-known metaheuristic methods, this paper discovers the efficiency of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Evolutionary PSO (EPSO) in solving the proposed RPP problem. The proposed approaches have been successfully tested on IEEE 14 bus system and a comparative study is illustrated.
Year
DOI
Venue
2007
10.1109/ICSMC.2007.4414140
Montreal, Que.
Keywords
Field
DocType
combinatorial mathematics,genetic algorithms,integer programming,load shedding,nonlinear programming,particle swarm optimisation,power system planning,reactive power,IEEE 14 bus system,combinatorial optimization problems,evolutionary particle swarm optimization,genetic algorithm,load shedding,metaheuristic methods,mixed integer nonlinear programming problem,optimal allocation,reactive power planning
Combinatorial optimization problem,Computer science,Nonlinear programming,AC power,Integer programming,Artificial intelligence,Genetic algorithm,Metaheuristic,Particle swarm optimization,Mathematical optimization,Algorithm,Machine learning,Load Shedding
Conference
ISSN
ISBN
Citations 
1062-922X
978-1-4244-0991-4
2
PageRank 
References 
Authors
0.48
4
4
Name
Order
Citations
PageRank
Mehdi Eghbal120.81
El-Araby, E.E.230.92
Naoto Yorino346.46
Yoshifumi Zoka423.86