IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1166-d1042822.html
   My bibliography  Save this article

A Visualization-Based Ramp Event Detection Model for Wind Power Generation

Author

Listed:
  • Junwei Fu

    (State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
    Zhejiang Energy Technology Research Institute Co., Ltd., Hangzhou 311121, China)

  • Yuna Ni

    (School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Yuming Ma

    (School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jian Zhao

    (School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Qiuyi Yang

    (School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Shiyi Xu

    (School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Xiang Zhang

    (School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
    Shangyu Science and Engineering Research Institute Co., Ltd. of Hangzhou Dianzi University, Shaoxing 312399, China)

  • Yuhua Liu

    (School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China
    Shangyu Science and Engineering Research Institute Co., Ltd. of Hangzhou Dianzi University, Shaoxing 312399, China)

Abstract

Wind power ramp events (WPREs) are a common phenomenon in wind power generation. This unavoidable phenomenon poses a great harm to the balance of active power and the stability of frequency in the power supply system, which seriously threatens the safety, stability, and economic operation of the power grid. In order to deal with the impact of ramp events, accurate and rapid detection of ramp events is of great significance for the formulation of response measures. However, some attribute information is ignored in previous studies, and the laws and characteristics of ramp events are difficult to present intuitively. In this paper, we propose a visualization-based ramp event detection model for wind power generation. Firstly, a ramp event detection model is designed considering the multidimensional attributes of ramp events. Then, an uncertainty analysis scheme of ramp events based on the confidence is proposed, enabling users to analyze and judge the detection results of ramp events from different dimensions. In addition, an interactive optimization model is designed, supporting users to update samples interactively, to realize iterative optimization of the detection model. Finally, a set of visual designs and user-friendly interactions are implemented, enabling users to explore WPREs, judge the identification results, and interactively optimize the model. Case studies and expert interviews based on real-world datasets further demonstrate the effectiveness of our system in the WPREs identification, the exploration of the accuracy of identification results, and interactive optimization.

Suggested Citation

  • Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1166-:d:1042822
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1166/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1166/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    2. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & Xiong, Yi & Huang, Heming, 2017. "Model of selecting prediction window in ramps forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 98-107.
    3. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    4. Li Han & Yan Qiao & Mengjie Li & Liping Shi, 2020. "Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning," Energies, MDPI, vol. 13(23), pages 1-19, December.
    5. Cui, Yang & He, Yingjie & Xiong, Xiong & Chen, Zhenghong & Li, Fen & Xu, Taotao & Zhang, Fanghong, 2021. "Algorithm for identifying wind power ramp events via novel improved dynamic swinging door," Renewable Energy, Elsevier, vol. 171(C), pages 542-556.
    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. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    2. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.
    3. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
    4. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    5. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    6. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    7. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    8. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    9. 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.
    10. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    11. António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
    12. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    13. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    14. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    15. Jabeur, Sami Ben & Ballouk, Houssein & Mefteh-Wali, Salma & Omri, Anis, 2022. "Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    17. Sizu Hou & Yisu Hou & Baikui Li & Ziqi Wang, 2023. "Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty," Energies, MDPI, vol. 16(12), pages 1-21, June.
    18. Henrik Zsiborács & Gábor Pintér & András Vincze & Nóra Hegedűsné Baranyai, 2022. "Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries," Sustainability, MDPI, vol. 14(24), pages 1-58, December.
    19. Marania Hopuare & Tao Manni & Victoire Laurent & Keitapu Maamaatuaiahutapu, 2022. "Investigating Wind Energy Potential in Tahiti, French Polynesia," Energies, MDPI, vol. 15(6), pages 1-13, March.
    20. Changfang Guo & Zhen Yang & Shen Li & Jinfu Lou, 2020. "Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach," Sustainability, MDPI, vol. 12(5), pages 1-15, February.

    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:16:y:2023:i:3:p:1166-:d:1042822. 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.