Paper Info
Title
Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems
Abstract
In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA-PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA-PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA-PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to -50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA-PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.
Year
DOI
Venue
2013
10.1016/j.asoc.2013.07.021
Appl. Soft Comput.
Keywords
Field
DocType
particle swarm optimization,optimization algorithm,hybrid bfoa-pso algorithm,automatic generation control,hbfoa-pso algorithm,tie-line power,non-linear power system model,power system,area system,area non-reheat thermal system,proposed hbfoa-pso
Particle swarm optimization,Mathematical optimization,Control theory,Swarm behaviour,PID controller,Control theory,Electric power system,Multi-swarm optimization,Robustness (computer science),Automatic Generation Control,Mathematics
Journal
Volume
Issue
ISSN
13
12
1568-4946
Citations 
PageRank 
References 
29
2.00
8
Authors
3
Name
Order
Citations
PageRank
Sidhartha Panda121124.23
Banaja Mohanty2312.41
P. K. Hota3292.33