|
|
 |
| |
|
|
|
Optimizing Firefly Algorithm for Directional Overcurrent Relay Coordination: A case study on the Impact of Parameter Settings |
|
|
|
PP: 3205-3227 |
|
|
|
doi:10.18576/isl/120745
|
|
|
|
Author(s) |
|
|
|
Tareq Foqha,
Samer Alsadi,
Ali Elrashidi,
Nael Salman,
|
|
|
|
Abstract |
|
|
| This paper investigates the application of the Firefly Algorithm for solving the coordination problem in the IEEE 3-bus network. It analyzes the impact of key parameters, including the number of generations, population size, absorption coefficient (γ), and randomization parameter (α), on the algorithms performance. Through extensive experimentation, the study demonstrates the impact on solution quality, feasibility, computational requirements, and efficiency. Results indicate that increasing the number of generations improves solution quality, but benefits diminish beyond a certain point. Feasibility improves with higher generations, but a balance between solution quality and feasibility becomes apparent at very high generations. Objective function evaluations and computation time increase linearly with generations. Larger population sizes yield better solution quality and feasibility, but a balance is observed at very high population sizes. Objective function evaluations and computation time scale proportionally with population size. The randomization parameter has a modest influence on performance, with no significant changes observed. However, extreme values impact solution quality, feasibility, and computation time. The absorption coefficient significantly affects convergence and solution quality. Lower values expedite convergence but may lead to suboptimal solutions, while higher values enhance exploration at the cost of increased computational effort. This study provides a comprehensive understanding of parameter selection and optimization in the Firefly Algorithm for solving the coordination problem of the IEEE 3-bus network, offering valuable guidance for future research in enhancing performance through parameter refinement and adaptive techniques.
|
|
|
|
|
|
|
|
