19 research outputs found
Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network
a b s t r a c t This work demonstrates the application of Genetic Algorithm (GA) technique for the simultaneous stabilization of power systems using a Unified Power Flow Controller (UPFC). The GA is applied to find the optimal location of the UPFC and to tune its control parameters under different operating conditions. The problem is formulated as a multiobjective optimization problem which aims at maximizing the damping ratio of the electromechanical modes using different lines fitted with the UPFC. The approach is successfully tested on the 16-machine 68-bus New England-New York interconnected system and on the Iraqi National Super Grid System (INSGS) to validate its effectiveness in the damping of local and inter-area modes of oscillations. In addition, the proposed approach demonstrated better performance when compared to a fuzzy-based UPFC damping controller
An offset-free multivariable model predictive control for quadruple tanks system
This paper addresses the design and implementation of a robust multivariable model predictive control (MPC) on a quadruple tanks system. Mismatch between the MPC\u27s model and the process may cause constraint violation, non-optimized performance and even instability. It is the objective of this paper to offset-free control the process in the presence of constraints and model mismatch. It is shown how this model mismatch is compensated by augmented state disturbances, and also how the steady state error is eliminated. In this method, an observer is designed to estimate the disturbances and states. The results show how the proposed control method increases the robustness of the model predictive controller in simulation and in real time implementation
Soft constrained finite horizon model predictive control
This article addresses tuning of the finite prediction horizon of soft-constrained model predictive control. The findings presented in this paper prove that there exists a finite horizon such that the infinite horizon soft-constrained model predictive control problem can be solved as a finite horizon soft-constrained model predictive control problem. Algorithms are proposed to compute the upper-bound of the prediction horizon in online and offline modes. The effectiveness of the proposed algorithm is verified by illustrative example
Multivariable Offset-Free Model Predictive Control for Quadruple Tanks System
The design and implementation of a robust multivariable model predictive control (MPC) on a quadruple tanks system (QTS) is addressed in this paper. Mismatch between the MPC\u27s model and the process may cause constraint violation, nonoptimized performance, and even instability. It is the objective of this paper to offset-free control the process in the presence of constraints and model mismatch. It is shown in this paper how this model mismatch is compensated by augmented state disturbances, and also how the steady-state error is eliminated. In the proposed method, an observer is designed to estimate the disturbances and states. The results show how the proposed control method increases the robustness of the model predictive controller in simulation and in real-time implementations on a new QTS proposed in this work together with techniques designed to identify the parameters of this novel plant
Stability of soft constrained finite horizon model predictive control
This paper addresses the 5 stability of soft-constrained model predictive control (MPC). It is shown that the infinite horizon soft-constrained MPC problem can be solved as a finite horizon soft-constrained MPC problem if the prediction horizon is greater than an upper bound. The contribution of this paper is a procedure to compute the prediction horizon upper bound, which guarantees the stability. The proposed technique is verified using two simulation examples. The second example (inverted pendulum) is verified through practical implementation