Skip to main content

Research Repository

Advanced Search

Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Ahmad, Tanvir; Basit, Abdul; Anwar, Juveria; Coupiac, Olivier; Kazemtabrizi, Behzad; Matthews, Peter

Fast Processing Intelligent Wind Farm Controller for Production Maximisation Thumbnail


Authors

Tanvir Ahmad

Abdul Basit

Juveria Anwar

Olivier Coupiac



Abstract

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( CP ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions

Citation

Ahmad, T., Basit, A., Anwar, J., Coupiac, O., Kazemtabrizi, B., & Matthews, P. (2019). Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Energies, 12(3), Article 544. https://doi.org/10.3390/en12030544

Journal Article Type Article
Acceptance Date Feb 6, 2019
Online Publication Date Feb 10, 2019
Publication Date Feb 10, 2019
Deposit Date Feb 13, 2019
Publicly Available Date Feb 13, 2019
Journal Energies
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 12
Issue 3
Article Number 544
DOI https://doi.org/10.3390/en12030544

Files

Published Journal Article (2.3 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).





You might also like



Downloadable Citations