Preprint Article Version 1 This version is not peer-reviewed

Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Version 1 : Received: 5 January 2019 / Approved: 8 January 2019 / Online: 8 January 2019 (11:34:39 CET)

How to cite: Ahmad, T.; Basit, A.; Akhtar, S.; Anwar, J.; Coupiac, O.; Kazemtabrizi, B.; Matthews, P.C. Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Preprints 2019, 2019010067 (doi: 10.20944/preprints201901.0067.v1). Ahmad, T.; Basit, A.; Akhtar, S.; Anwar, J.; Coupiac, O.; Kazemtabrizi, B.; Matthews, P.C. Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Preprints 2019, 2019010067 (doi: 10.20944/preprints201901.0067.v1).

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.

Subject Areas

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

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