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153 / 2023-10-19 19:48:13
A RNN-based Photovoltaic Power Identification Method for Distribution Networks
Recurrent Neural Network,nonlinear mapping,photovoltaic output power identification
1.Renewable energy system > (2)、Renewable energy system planning, design, and operation
Final Paper
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
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.

 
Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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