Investigation of active failure detection algorithms
Abstract (Summary)
HANNAS, BENJAMIN LEE. Investigation of Active Failure Detection Algorithms.
(Under the direction of Dr. Stephen L. Campbell.)
This study analyzes two robust failure detection algorithms and applies the algorithms
to three power system models. An optimal test signal to distinguish between a
failure model and a normal model is calculated using the two algorithms. Advantages
and disadvantages of each algorithm, Direct Optimization (DO) and Constrained
Control (CC), are discussed. DO uses complex software (Sparse Optimal Control
Software by The Boeing Corporation) to solve the necessary and boundary conditions
of the optimization problem directly. CC utilizes free software (SciLab by Inria,
Enpc.) to solve a two-point boundary value problem based on the necessary and
boundary conditions of the optimization problem. Both algorithms yield similar signals,
but DO is faster and more accurate yet requires expensive software. CC is not
as robust, but can be run on free software and does not need as much fine tuning as
the DO algorithm. Examples presented are two DC motor models and a linearized
gas turbine model.
Investigation of Active Failure
Detection Algorithms
by
Bibliographical Information:
Advisor:
School:North Carolina State University
School Location:USA - North Carolina
Source Type:Master's Thesis
Keywords:north carolina state university
ISBN:
Date of Publication: