Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Tanvir Ahmad
* ORCID logo ,
Abdul Basit
ORCID logo ,
Samia Akhtar
,
Juveria Anwar
,
Olivier Coupiac
,
Behzad Kazemtabrizi
,
Peter C. Matthews
Version 1 : Received: 5 January 2019 / Approved: 8 January 2019 / Online: 8 January 2019 (11:34:39 CET)

A peer-reviewed article of this Preprint also exists.

Ahmad, T.; Basit, A.; Anwar, J.; Coupiac, O.; Kazemtabrizi, B.; Matthews, P.C. Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Energies 2019, 12, 544. Ahmad, T.; Basit, A.; Anwar, J.; Coupiac, O.; Kazemtabrizi, B.; Matthews, P.C. Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Energies 2019, 12, 544.

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 ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized 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 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Keywords

wind farm production maximisation; coordinated control; $C_P$-based optimisation; yaw-based optimisation; wake effects; turbulence intensity; Jensen model; particle swarm optimisation

Subject

Engineering, Energy and Fuel Technology

Copyright: 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.

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