Reduced order modelling of linear MIMO systems using genetic algorithm

Abstract

In this paper, an algorithm for order reduction of linear multivariable systems is proposed using the combined advantages of the dominant pole retention method and the error minimization by Genetic algorithm. The denominator of the reduced order transfer function matrix is obtained by retaining the dominant poles of the original system while the numerator terms of the lower order transfer matrix are determined by minimizing the integral square error in between the transient responses of original and reduced order models using Genetic algorithm. Each element of the transfer function matrix of the original system is considered separately. The reduction procedure is simple and computer oriented. The proposed algorithm guarantees stability of the reduced order transfer function matrix if the original high order system is stable and is having superior features, including easy implementation and good computational efficiency. The proposed algorithm has been applied successfully to the transfer function matrix of a 10 th order two-input two-output linear time invariant model of a practical power system. The performance of the algorithm is tested by comparing the relevant computer simulation results.

Application and Comparative Analysis of Various Classical and Soft Computing Techniques for Model Reduction of MIMO Systems

“…Recently soft computing techniques like GA [23], modified GA [24], PSO [5] are popular in solving optimization problems. Soft computing techniques are combined with classical methods for model reduction to address real world applications which are having thousand of parameters.…”

“…a standard IEEE type-I exciter with rate feedback and power system stabilizer (PSS). The numerical values of the parameters which define the total operating systems as well as the operating point are given in [23]. Based on the state variables, parameters values and the operating point of the system (without accounting for the limiters) may be described in the form of state space as:…”

“…The numerical values of the state space matrices A, B and C are obtained by substituting the data given in [23]. The transfer function matrix of the 10th order two-input two-output of linear time invariant power system model after converting state space matrices is given by The reduced order transfer function matrix for the original order two-input two-output is given by…”

Improved Pade-Pole Clustering Approach using Genetic Algorithm for Model Order Reduction

“…Author combined this technique with Genetic Algorithm for better an optimized result. There are so many approaches for the stable reduced order models presented in literature in SISO system or MIMO with mixed method [10][11][12][13][14][15][16][17] and allot work in time domain as well as in frequency domain with Genetic Algorithm.…”

Generalized Two Axes Modeling, Order Reduction and Numerical Analysis of Squirrel Cage Induction Machine for Stability Studies

“…As induction motors are becoming day by day a main component of A.C drive systems, a detailed study of the different aspects of its modeling draws the attention of the researchers [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. The important approach of modeling of induction machine is the Park's transformation theory, which converts the machine from phase model to axis model.…”