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340 / 2021-11-10 09:27:08
Convolutional Deep Leaning-Based Distribution System Topology Identification with Renewables
Distribution system topology identification, correlation, deep learning, renewable energy
Power system and automation > Smart grid and utility applications
Final Paper
Huayi Wu / The Hong Kong Polytechnic University
Zhao Xu / The Hong Kong Polytechnic University
Minghao Wang / The Hong Kong Polytechnic University
Obtaining the distribution system topology states timely is critical for system monitoring while challenged by correlations brought by high penetrated renewable energy sources (RES). To address this issue, a deep learning model is proposed for distribution system topology identification considering the underlying complex correlations of renewables. Specifically, to remove the dependence of the power system model parameters like line impedance, the input of the model only consists of the voltage magnitudes. Then, this is fed into the proposed deep learning model (DLM), which can fully capture the data features and thus classify the topology of the grid to hedge against the correlations of the RES and thus enhance the identification accuracy. The simulation results demonstrate the accuracy and efficiency of the proposed model in the IEEE 33-node distribution system.
Important Date

  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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