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Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

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  • Bilal, Boudy
  • Adjallah, Kondo Hloindo
  • Sava, Alexandre
  • Yetilmezsoy, Kaan
  • Kıyan, Emel

Abstract

This study proposes an original adaptive neuro-fuzzy inference system modeling approach to predict the output power of a wind turbine. The model's input includes the wind speed, turbine rotational speed, and mechanical-to-electrical power converter's temperature. The structure of the adaptive neuro-fuzzy inference system-based model was first identified using the processed data gathered from wind turbine number 1 of a 30-MW wind farm in Nouakchott (Mauritania). Then, the proposed data-driven model was trained and validated according to two new scenarios based on the data set from four identical wind turbines operated in the same climatic conditions and the data set from the same wind turbines operated under different climatic conditions. Benchmarking involved the proposed model, existing approaches in the literature, and five adaptive neuro-fuzzy inference system-based models, including grid partition, subtractive clustering, fuzzy C-means clustering, genetic algorithm, and particle swarm optimization, on the same data set to validate their prediction performance. Compared with existing adaptive neuro-fuzzy inference system-based models, the proposed approach was proven to be a promising methodology with higher accuracy for estimating the output power of wind turbines operating in different climatic conditions. According to the results from two different scenarios, the lowest value of the fitting rate and the highest values of the normalized mean square error, normalized mean absolute error, and root mean square error for the validating period were 0.9977, 0.0047, 0.0473, and 46.5831 kW, respectively. Moreover, the proposed model showed superior forecasting performance and thus better accuracy in estimating wind power output compared to other adaptive neuro-fuzzy inference system-based models.

Suggested Citation

  • Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Kıyan, Emel, 2022. "Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023379
    DOI: 10.1016/j.energy.2021.122089
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    2. Li Yan & Zhengyu Zhu & Xiaopeng Kang & Boyang Qu & Baihao Qiao & Jiajia Huan & Xuzhao Chai, 2022. "Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-22, July.
    3. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    4. Asmaa Fawzy & Youssef Mobarak & Dina S. Osheba & Mahmoud G. Hemeida & Tomonobu Senjyu & Mohamed Roshdy, 2022. "An Online Archimedes Optimization Algorithm Identifier-Controlled Adaptive Modified Virtual Inertia Control for Microgrids," Energies, MDPI, vol. 15(23), pages 1-27, November.

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