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Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Author

Listed:
  • Tanvir Ahmad

    (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)

  • Abdul Basit

    (US Pakistan Center for Advanced Studies in Energy, University of Engineering and Technology (UET), Peshawar 25000, Pakistan)

  • Juveria Anwar

    (US Pakistan Center for Advanced Studies in Energy, University of Engineering and Technology (UET), Peshawar 25000, Pakistan)

  • Olivier Coupiac

    (Engie Green, 59777 Lille, France)

  • Behzad Kazemtabrizi

    (School of Engineering, Durham University, Durham DH1 3LE, UK)

  • Peter C. Matthews

    (School of Engineering, Durham University, Durham DH1 3LE, UK)

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. 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.

Suggested Citation

  • Tanvir Ahmad & Abdul Basit & Juveria Anwar & Olivier Coupiac & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation," Energies, MDPI, vol. 12(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:544-:d:204583
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    References listed on IDEAS

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    1. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    2. Lignarolo, L.E.M. & Ragni, D. & Krishnaswami, C. & Chen, Q. & Simão Ferreira, C.J. & van Bussel, G.J.W., 2014. "Experimental analysis of the wake of a horizontal-axis wind-turbine model," Renewable Energy, Elsevier, vol. 70(C), pages 31-46.
    3. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
    4. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    5. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
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    Cited by:

    1. Tanvir Ahmad & Abdul Basit & Muneeb Ahsan & Olivier Coupiac & Nicolas Girard & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
    2. Jim Kuo & Kevin Pan & Ni Li & He Shen, 2020. "Wind Farm Yaw Optimization via Random Search Algorithm," Energies, MDPI, vol. 13(4), pages 1-15, February.
    3. Yanfang Chen & Young Hoon Joo & Dongran Song, 2022. "Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    5. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    6. Javier Serrano González & Bruno López & Martín Draper, 2021. "Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model," Energies, MDPI, vol. 14(4), pages 1-18, February.
    7. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.

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