Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance

Simple item page
dc.contributor.authorLi, Wei
dc.contributor.authorPandit, Ravi Kumar
dc.date.accessioned2024-11-28T13:57:23Z
dc.date.available2024-11-28T13:57:23Z
dc.date.freetoread2024-11-28
dc.date.issued2024-12
dc.date.pubOnline2024-11-11
dc.description.abstractWind energy is a significant renewable resource, but its efficient harnessing requires advanced control systems. This study presents a Data-Centric Predictive Control (DPC) system, enhanced by a Tuna Swarm Optimization-Backpropagation Neural Network (TSO-BPNN) for predictive wind turbine control. It's like a smart tool that uses innovative fusion of deep learning, predictive Control, and reinforcement learning. Unlike traditional control methods, the proposed approach uses real-time data to optimize turbine performance in response to fluctuating wind conditions. The system is validated using simulations on the FAST platform, which demonstrate its superior performance in two critical operational regions. Specifically, in Region II, where the objective is to maximize power extraction from the wind, the DPC achieves a 1.07 % reduction in overshoot and an improvement of 36.14 units in steady-state error compared to traditional methods. The response time remains comparable to existing Model Predictive Control (MPC) strategies, ensuring real-time applicability without sacrificing efficiency. In Region III, where maintaining constant power output is crucial, the DPC outperforms both the baseline and MPC methods, reducing overshoot by 0.58 % and improving accuracy by 17.27 units compared to the baseline method. These results highlight the effectiveness of the proposed DPC system in optimizing turbine performance under variable wind conditions, offering a significant improvement over traditional methods in both accuracy and control precision.
dc.description.journalNameRenewable Energy
dc.identifier.citationLi W, Pandit RK. (2024) Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance. Renewable Energy, Volume 237, Part C, December 2024, Article number 121821
dc.identifier.eissn1879-0682
dc.identifier.elementsID558567
dc.identifier.issn0960-1481
dc.identifier.paperNo121821
dc.identifier.urihttps://doi.org/10.1016/j.renene.2024.121821
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23235
dc.identifier.volumeNo237
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0960148124018895?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep neural networks
dc.subjectTSO-BPNN
dc.subjectModel predictive control
dc.subjectReinforcement learning
dc.subjectWind turbine control
dc.subject4007 Control Engineering, Mechatronics and Robotics
dc.subject40 Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject7 Affordable and Clean Energy
dc.subjectEnergy
dc.subject40 Engineering
dc.titleData-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-11-04

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Data-centric_predictive_control-2024.pdf
Size:
9.74 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)