摘要详情

ID / 提交时间

340 / 2021-11-10 09:27:08

标题

Convolutional Deep Leaning-Based Distribution System Topology Identification with Renewables

关键字

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

主题及专题

Power system and automation > Smart grid and utility applications

状态

终稿

作者

Huayi Wu / The Hong Kong Polytechnic University

Zhao Xu / The Hong Kong Polytechnic University

Minghao Wang / The Hong Kong Polytechnic University

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.

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