Disturbance Rejection and Uncertainty Analysis in Wind Turbines Using Model Predictive Control

Alok Kumar and Atul Kelkar ()
Additional contact information
Alok Kumar: Mechanical Engineering Department, Clemson University, Clemson, SC 29631, USA
Atul Kelkar: Mechanical Engineering Department, Clemson University, Clemson, SC 29631, USA

Energies, 2025, vol. 18, issue 10, 1-19

Abstract: For effective wind turbine operations, it is essential to maintain the power limit and reduce the stress on the drive train in the presence of disturbance and uncertain conditions. In our work, we propose a Model Predictive Control (MPC) framework with quadratic cost functions, incorporating control input and state constraints to mitigate the challenge of disturbance rejection and uncertainty analysis for the wind turbine operation. We have tailored the algorithm to the practical parameters of the National Renewable Energy Laboratory’s (NREL) Controls Advanced Research Turbine (CART) model. We illustrate the impact of wind disturbances on achieving the optimal control law and evaluate the performance of integral MPC in disturbance rejection for the wind turbine operation, comparing it with the constrained optimal control law outcomes. The simulation results also show the efficacy of integral MPC for the uncertainty in the initial conditions of the wind turbines. This is shown by the propagation of the first two moments, i.e., mean and variance, for the states of the wind turbine. Further, we obtained the control law and mean–variance propagation for the variation in disturbance intensity. The overall results prove the efficacy of using the MPC framework for uncertainty analysis and disturbance rejection to obtain optimal operation in wind turbines.

Keywords: disturbance rejection; Model Mredictive Control; uncertainty analysis; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/10/2504/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/10/2504/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:10:p:2504-:d:1654601

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().