Saleh Saraireh

Substructure Preservation Sylvester-based Model Order Reduction with Application to Power Systems

Abstract A new substructure preservation Sylvester-based model order reduction technique with application to power systems is presented in this article. The new approach is intended for multiple-input–multiple-output linear time invariant systems, given in the form of state-space realization with the objective of obtaining a proper reduced-order model (complexity reduction), preserving the dominant eigenvalues of the full-order model as a subset in the reduced model, and maintaining a minimum steady-state error. The proposed reduction method is performed based on transforming the system state matrix into a special form, taking into account the dominant eigenvalues, while the rest of the model transformation is derived utilizing the Sylvester equation formula. Once the system is transformed, the reduced-order model is obtained by truncating the less dominant eigenvalues using the singular perturbation technique. To evaluate the potential of the new approach, results of the proposed technique are compared to some of the well-known methods for model order reduction and relatively recently published work. Results comparison shows the superiority of the new method especially in terms of time convergence.

Soft Computing Techniques for Reduced Order Modelling: Review and Application

AbstractAs the mathematical procedure of system modelling often leads to a comprehensive description, which causes significant difficulty in both analysis and control synthesis, it is necessary to find lower order models, which maintain the dominant characteristics of the original system. In this paper, different soft computing (named as artificial intelligence (AI)) techniques are presented, applied, and analysed for model order reduction (MOR) of multi time scale systems with the objective of substructure preservation. In addition to that, we investigate the firefly optimization technique for MOR with substructure preservation. The analysis is concerned with the optimization approach and quality of method performance.