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Identification and Correction Method of Bad Data of Renewable Energy Plants with Deep Learning
编号：320 访问权限：仅限参会人 更新：2022-05-20 09:40:15 浏览：249次 张贴报告

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摘要
Given the problem of real-time data acquisition errors in renewable energy plants, the data of the renewable energy plants have the characteristics of mass and mutually coupled characteristics, a deep learning-based method for identifying and correcting bad data from renewable energy plants is proposed. Firstly, a deep neural network identification model is constructed to identify the real-time bad data, and the bad data of the real-time identification was obtained. Secondly, the BP neural network correction model was constructed to correct the bad data of the identification, and the reliable data of the operation of the renewable energy station is obtained. Finally, the accuracy and effectiveness of the proposed method are verified through the analysis of the real historical data of a typical wind farm.
 
关键词
renewable energy;deep learning;data identification;data correction;deep neural networks
报告人
LiuZhen
Hohai University

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重要日期

  • 会议日期

    05月27日

    2022

    至

    05月29日

    2022

  • 02月28日 2022

    初稿截稿日期

  • 05月29日 2022

    注册截止日期

  • 06月22日 2022

    报告提交截止日期

主办单位
IEEE Beijing Section
China Electrotechnical Society
Southeast University
协办单位
IEEE Industry Applications Society
IEEE Nanjing Section
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