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A RNN-based Photovoltaic Power Identification Method for Distribution Networks
编号：163 访问权限：仅限参会人 更新：2023-11-20 13:53:24 浏览：469次 张贴报告

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摘要
To ensure the safe operation of distribution networks with more and more photovoltaic (PV), it is crucial to study the PV output power identification technology. Thus, the ability of the system to withstand random fluctuations will be enhanced and the stability of distribution network will also be improved effectively. In this paper, a standard three-layer recurrent neural network (RNN) is proposed to build a nonlinear mapping model for efficiently identifying PV power. RNN can fit the nonlinear mapping relationship between the intensity of illumination and PV output power within the distribution station area effectively. The distributed PV output power from the power measurement data of the distribution station area is successfully separated. Finally, a case study of Yulara Solar power plant with 1.8 MVA in Australia is used to validate the feasibility and effectiveness of the proposed method.
 
关键词
Recurrent Neural Network,nonlinear mapping,photovoltaic output power identification
报告人
Shihan Wang
student Hunan University

稿件作者
Can Wang State Grid Hunan Electric Power Company Limited Research Institute
Shihan Wang Hunan University
Yong Li Hunan University
Yanjian Peng Hunan University
Chang Li Hunan University
Jiayan Liu Hunan University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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