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Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

A. Parassuram1,*, P. Somasundaram1
Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Guindy, Chennai, Tamilnadu, India.
*Corresponding Author: A. Parassuram. Email: .

Computer Modeling in Engineering & Sciences 2018, 117(2), 169-187. https://doi.org/10.31614/cmes.2018.01720

Abstract

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control (LFC). A dynamic model of an interconnected power system was used for Model Predictive Controller (MPC) design. MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix. In this paper, the optimal closed loop feedback gain matrix was calculated using Kautz function. Being an Orthonormal Basis Function (OBF), Kautz function has an advantage of solving complex pole-based nonlinear system. Genetic Algorithm (GA) was applied to optimally tune the Kautz function-based MPC. A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC. The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral (PI) controller and Linear Quadratic Regulation (LQR).

Keywords

Load frequency control, model predictive controller, orthonormal basis function, kautz function, phase plane analysis, linear quadratic regulator, proportional and integral controller, genetic algorithm.

Cite This Article

Parassuram, A., Somasundaram, P. (2018). Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System. CMES-Computer Modeling in Engineering & Sciences, 117(2), 169–187.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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