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Neural Computing and Applications

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20-04-2019 | Original Article

A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines

Authors: Mohamed A. Mohamed, Ahmed A. Zaki Diab, Hegazy Rezk, Tao Jin

Published in: Neural Computing and Applications | Issue 11/2020

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Abstract

With the rapid growth of renewable energy resources, wind energy system is getting more interest everywhere throughout the world. However, its extensive use in power systems prompts many power system dynamics and stability problems. Load variation and anomalous operating conditions prompt inconsistencies in frequency and planned power trades. These inconsistencies should be remedied by load frequency control. This paper introduces a novel frequency control system utilizing a mix of adaptive model predictive controller (AMPC) and recursive polynomial model estimator (RPME) integrated with double fed induction generator wind turbines. Inside each control duration, the RPME is identifying a discrete-time online autoregressive exogenous model. The latter is used through the AMPC to update the interior plant model in order to achieve a successful nonlinear control. The performance of the proposed system has been verified and contrasted with the conventional MPC system through a computer simulation-based MATLAB/SIMULINK. The simulation results demonstrated the superiority of the proposed system as for the conventional MPC system.

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Title: A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines
Authors: Mohamed A. Mohamed
Ahmed A. Zaki Diab
Hegazy Rezk
Tao Jin
Publication date: 20-04-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04205-w

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