IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipbs0360544221023379.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221023379
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    2. Manobel, Bartolomé & Sehnke, Frank & Lazzús, Juan A. & Salfate, Ignacio & Felder, Martin & Montecinos, Sonia, 2018. "Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 125(C), pages 1015-1020.
    3. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    4. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    5. Çevik, Hasan Hüseyin & Çunkaş, Mehmet & Polat, Kemal, 2019. "A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    7. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    8. Alma Y. Alanis & Oscar D. Sanchez & Jesus G. Alvarez, 2021. "Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    9. Wang, Gang & Jia, Ru & Liu, Jinhai & Zhang, Huaguang, 2020. "A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning," Renewable Energy, Elsevier, vol. 145(C), pages 2426-2434.
    10. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    11. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
    2. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    3. Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).
    4. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
    5. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
    6. Díaz, Santiago & Carta, José A. & Castañeda, Alberto, 2020. "Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control," Renewable Energy, Elsevier, vol. 159(C), pages 812-826.
    7. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
    8. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.
    9. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
    10. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
    11. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    12. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
    13. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    14. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
    15. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).
    16. Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
    17. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
    18. Liu, Lei & Liu, Jicheng & Ye, Yu & Liu, Hui & Chen, Kun & Li, Dong & Dong, Xue & Sun, Mingzhai, 2023. "Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty," Renewable Energy, Elsevier, vol. 205(C), pages 598-607.
    19. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    20. Wang, Fei & Chen, Peng & Zhen, Zhao & Yin, Rui & Cao, Chunmei & Zhang, Yagang & Duić, Neven, 2022. "Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method," Applied Energy, Elsevier, vol. 323(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.