Investigation of Active Failure Detection Algorithms

failure detection;power systems;control theory;detection signal;additive noise;noise bound;stiff;numeric solvers

Hannas, Benjamin L ; Dr. Stephen L. Campbell, Committee Chair,Dr. Mette S. Olufsen, Committee Member,Dr. Hien T. Tran, Committee Member,Hannas, Benjamin L ; Dr. Stephen L. Campbell ; Committee Chair ; Dr. Mette S. Olufsen ; Committee Member ; Dr. Hien T. Tran ; Committee Member

University:North Carolina State University

关键词: failure detection;    power systems;    control theory;    detection signal;    additive noise;    noise bound;    stiff;    numeric solvers;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2023/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

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.