Generating random power grids for the verification of new load flow solvers
Hug, Adam
Loading…
Permalink
https://hdl.handle.net/2142/50397
Description
Title
Generating random power grids for the verification of new load flow solvers
Author(s)
Hug, Adam
Issue Date
2014-09-16
Director of Research (if dissertation) or Advisor (if thesis)
Goddard, Lynford L.
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)
power grid topology
random power grid
generating power grid
random graph
random network
small-world
small world
power flow
load flow
numerical solver
verification
Abstract
The purpose of this thesis is to expand the rigor of the development of new power flow solvers through graph generation. The use of the IEEE standard test cases as benchmarks is commonplace in literature, where they are used to demonstrate the effectiveness of new algorithms. This results in the use of as little as two to five grids with only tens or hundreds of nodes each. The sample size for these tests is very small and cannot fully represent the behavior of the algorithms being tested. Since this problem stems from the lack of real, publicly available grids, a solution is to generate power grids with the necessary components.
This thesis is the first to compare the performance of numerical methods in this setting. Two popular numerical methods are considered: the Newton-Raphson (NR) and Fast Decoupled Load Flow (FDLF) methods. It is found that with a modern direct matrix solver, NR is more efficient and robust than the FDLF when tested over several different topological factors. The results and methodology presented herein are used to test the speed and robustness of algorithms that solve similar power system problems such as Optimal Power Flow.
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.
