Fast Processing Intelligent Wind Farm Controller for Production Maximisation
US Pakistan Center for Advanced Studies in Energy, University of Engineering and Technology (UET), Peshawar 25000, Pakistan
School of Engineering, Durham University, Durham DH1 3LE, UK
Engie Green, 59777 Lille, France
Author to whom correspondence should be addressed.
Received: 26 December 2018 / Revised: 2 February 2019 / Accepted: 6 February 2019 / Published: 10 February 2019
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 (
) 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.
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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 PC. Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Energies. 2019; 12(3):544.
Ahmad, Tanvir; Basit, Abdul; Anwar, Juveria; Coupiac, Olivier; Kazemtabrizi, Behzad; Matthews, Peter C. 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation." Energies 12, no. 3: 544.
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