Abstract
In this paper, the technique applicable to reduce a high-order system to a low-order system is presented. The methods used are Routh stability array (RSA) method and stability equation (SE) method to get the reduced model of systems. The application of the techniques is examined over SISO linear time invariant systems and extended to MIMO systems. The step response performance of the reduced models gets compared to the original system as well as reduced models in literature in terms of rise-time, settling-time, peak-time and peak of the system. The comparative study reveals that the performances of the reduced models using proposed RSA and SE methods are encouraging as compared to that of with reduced models in literature.
Highlights
Nowadays, system becomes very complicated, tedious & costly by increases the use of some external controlling devices and the calculation, complexity, cost becomes major factor and creates a problem for analyst or programmer to solve those high-order system (HOS) problems [1, 2]
This transfer function for the simplicity or for ease of operation must be reduced to a lower-order transfer function using a reduction technique prevalent in literature such as Routh approximation [5], Pade approximation [6], Routh-Pade method [7, 8], Stability equation method [9, 10, 11, 12], Differentiation method [2, 3, 4, 13], Routh Stability array method [10, 14], pole clustering [15], integral square error method [1] and/or based on soft computing techniques such as genetic algorithm (GA) [16, 17], particle swarm optimization (PSO) [18, 19], bat algorithm (BA)[1] and Harmony search algorithm [7] etc
The authors have screened the literature for second order reduced models using different techniques of the system
Summary
System becomes very complicated, tedious & costly by increases the use of some external controlling devices and the calculation, complexity, cost becomes major factor and creates a problem for analyst or programmer to solve those high-order system (HOS) problems [1, 2]. All the control system and power system networks defined in MATLAB using a block diagram in SIMULINK portion may represent a higher-order transfer function for that system [4] This transfer function for the simplicity or for ease of operation must be reduced to a lower-order transfer function using a reduction technique prevalent in literature such as Routh approximation [5], Pade approximation [6], Routh-Pade method [7, 8], Stability equation method [9, 10, 11, 12], Differentiation method [2, 3, 4, 13], Routh Stability array method [10, 14], pole clustering [15], integral square error method [1] and/or based on soft computing techniques such as genetic algorithm (GA) [16, 17], particle swarm optimization (PSO) [18, 19], bat algorithm (BA)[1] and Harmony search algorithm [7] etc. The application of model order reduction techniques have been considered for reduction of single-machine infinite-bus (SMIB) power system in [21, 20, 10, 22, 23]
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