PSO-based model predictive control for load frequency regulation with wind turbines
With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context...
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sg-ntu-dr.10356-1686282023-06-16T15:40:01Z PSO-based model predictive control for load frequency regulation with wind turbines Fan, Wei Hu, Zhijian Veerasamy, Veerapandiyan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Wind Turbines Load Frequency Control With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies. Published version 2023-06-12T07:56:04Z 2023-06-12T07:56:04Z 2022 Journal Article Fan, W., Hu, Z. & Veerasamy, V. (2022). PSO-based model predictive control for load frequency regulation with wind turbines. Energies, 15(21), 8219-. https://dx.doi.org/10.3390/en15218219 1996-1073 https://hdl.handle.net/10356/168628 10.3390/en15218219 2-s2.0-85141881405 21 15 8219 en Energies © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
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Engineering::Electrical and electronic engineering Wind Turbines Load Frequency Control Fan, Wei Hu, Zhijian Veerasamy, Veerapandiyan PSO-based model predictive control for load frequency regulation with wind turbines |
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With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Fan, Wei Hu, Zhijian Veerasamy, Veerapandiyan |
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Article |
author |
Fan, Wei Hu, Zhijian Veerasamy, Veerapandiyan |
author_sort |
Fan, Wei |
title |
PSO-based model predictive control for load frequency regulation with wind turbines |
title_short |
PSO-based model predictive control for load frequency regulation with wind turbines |
title_full |
PSO-based model predictive control for load frequency regulation with wind turbines |
title_fullStr |
PSO-based model predictive control for load frequency regulation with wind turbines |
title_full_unstemmed |
PSO-based model predictive control for load frequency regulation with wind turbines |
title_sort |
pso-based model predictive control for load frequency regulation with wind turbines |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/168628 |
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1772828629829419008 |