Convolutional Deep Leaning-Based Distribution System Topology Identification with Renewables 2nd China International Youth Conference on Electrical Engineering CIYCEE 2021

报告详情

Convolutional Deep Leaning-Based Distribution System Topology Identification with Renewables

编号：61 访问权限：仅限参会人更新：2021-12-04 17:21:24 浏览：600次张贴报告

报告开始：2021年12月17日 15:45（Asia/Shanghai）

报告时间：5min

所在会场：[Z] Poster Session [Z9] Poster Session 9: Power system and automation

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摘要

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.

关键词

Distribution system topology identification, correlation, deep learning, renewable energy

报告人

Huayi Wu

The Hong Kong Polytechnic University

稿件作者

Huayi Wu The Hong Kong Polytechnic University

Zhao Xu The Hong Kong Polytechnic University

Minghao Wang The Hong Kong Polytechnic University

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重要日期

会议日期

07月11日

2023

至

08月18日

2023
11月10日 2021

初稿截稿日期
12月10日 2021

注册截止日期
12月11日 2021

报告提交截止日期

主办单位

IEEE IAS

承办单位

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