IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i11p4132-d831661.html
   My bibliography  Save this article

Dynamic Error Correction Method in Tachometric Anemometers for Measurements of Wind Energy

Author

Listed:
  • Paweł Ligęza

    (Strata Mechanics Research Institute, Polish Academy of Sciences, Reymonta 27, 30-059 Krakow, Poland)

Abstract

Measurements of air flow velocity are essential at every stage of the design, construction and operation of wind turbines. One of the basic measurement tools in this area is the tachometric anemometer, which is based on the simple physical phenomenon of the air kinetic energy exchange with a rotating measuring element. Tachometric anemometers have favorable operational features and good static metrological parameters. However, in the case of fast-changing flows, the measurement is burdened with a significant dynamic error, and the measured average value of the velocity is overestimated. This article presents the concept and results of pilot studies of a dynamic error correction method of tachometric anemometers. The correction consists of the precise measurement of the rotor’s rotational velocity and determination of the measured air velocity, taking into account the dynamics of the instrument. The developed method can be used in tachometric anemometers intended for laboratory, technical and industrial measurements in time-varying flows. One of the important application areas is the measurement of wind energy.

Suggested Citation

  • Paweł Ligęza, 2022. "Dynamic Error Correction Method in Tachometric Anemometers for Measurements of Wind Energy," Energies, MDPI, vol. 15(11), pages 1-9, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4132-:d:831661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/11/4132/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/11/4132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
    3. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Md. Mahbub Alam, & Al-Hadhrami, L.M. & Lashin, A., 2016. "Performance evaluation of cup-anemometers and wind speed characteristics analysis," Renewable Energy, Elsevier, vol. 86(C), pages 733-744.
    5. Santiago Pindado & Alfredo Sanz & Alain Wery, 2012. "Deviation of Cup and Propeller Anemometer Calibration Results with Air Density," Energies, MDPI, vol. 5(3), pages 1-19, March.
    Full references (including those not matched with items on IDEAS)

    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. Kim, Daeyoung & Ryu, Geonhwa & Moon, Chaejoo & Kim, Bumsuk, 2024. "Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm," Applied Energy, Elsevier, vol. 373(C).
    2. Paweł Ligęza, 2021. "Basic, Advanced, and Sophisticated Approaches to the Current and Forecast Challenges of Wind Energy," Energies, MDPI, vol. 14(23), pages 1-10, December.
    3. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    4. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    5. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    6. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    7. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    8. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    9. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    10. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    11. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Kevin Tomsovic, 2022. "An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-20, September.
    12. Minan Tang & Wenjuan Wang & Jiandong Qiu & Detao Li & Linyuan Lei, 2022. "Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales," Energies, MDPI, vol. 15(24), pages 1-21, December.
    13. Hanifi, Shahram & Cammarono, Andrea & Zare-Behtash, Hossein, 2024. "Advanced hyperparameter optimization of deep learning models for wind power prediction," Renewable Energy, Elsevier, vol. 221(C).
    14. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).
    15. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
    16. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    17. Geon Hwa Ryu & Young-Gon Kim & Sung Jo Kwak & Man Soo Choi & Moon-Seon Jeong & Chae-Joo Moon, 2022. "Atmospheric Stability Effects on Offshore and Coastal Wind Resource Characteristics in South Korea for Developing Offshore Wind Farms," Energies, MDPI, vol. 15(4), pages 1-23, February.
    18. Elena Roibas-Millan & Javier Cubas & Santiago Pindado, 2017. "Studies on Cup Anemometer Performances Carried out at IDR/UPM Institute. Past and Present Research," Energies, MDPI, vol. 10(11), pages 1-17, November.
    19. Takuji Matsumoto & Yuji Yamada, 2023. "Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power," Energies, MDPI, vol. 16(7), pages 1-22, March.
    20. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(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:gam:jeners:v:15:y:2022:i:11:p:4132-:d:831661. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.