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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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