Optimization algorithms for loading military diesel generators
Peterson, Nathan
Loading…
Permalink
https://hdl.handle.net/2142/102499
Description
Title
Optimization algorithms for loading military diesel generators
Author(s)
Peterson, Nathan
Issue Date
2018-12-07
Director of Research (if dissertation) or Advisor (if thesis)
Sauer, Peter W.
Johnson, Melanie
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Economic Load Dispatch
ELD
Particle Swarm Optimization
PSO
Cuckoo Search
CS
CSO
Bat algorithm
BA
First Fit Decreasing
FFD
generator
optimization algorithm
swarm
metaheuristic
Abstract
The economic load dispatch (ELD) problem challenges the designer to adequately provide for electrical load demand while minimizing operational costs. The military has a unique set of constraints for meeting the ELD problem to provide power to soldiers in forward operating bases. The constraints include the use of military diesel gensets that remain disconnected from each other and are loaded below a user-defined real power threshold (for a reliability safety cushion). In addition, the system must be simple enough to be constructed with minimal training and require no reconfiguration once established. As a result, a simple tool to quickly assign loads to isolated military diesel generators is required. To meet this need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the optimization algorithms can discover reasonably optimal solutions while substantially decreasing search time. For this application, FFD has more optimal average solutions as well as faster run time compared to the other algorithms.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
