A Data-driven Identification Method for New Energy Grid-connected Fault Features Based on Convolutional Neural Network-Reference-Cited by-同舟云学术

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A Data-driven Identification Method for New Energy Grid-connected Fault Features Based on Convolutional Neural Network

Published:2024-05-17 Issue: Volume:000 Page:65-68
ISSN:
Container-title:2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET)
language:
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Author:

Guan Zhaoxiong¹,Huangfu Hancong¹,Song Caihua¹

Affiliation:

1. Guangdong Power Grid Foshan Power Supply Bureau,Foshan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10625771/10625773/10626871.pdf?arnumber=10626871

Reference13 articles.

1. Hybrid cascaded MLI development for PV‐grid connection applications

2. Analysis of Wind Power Grid Connection Technology and Power Control[J];Guoyan;Analysis of the Prefabricated Construction Technology of Building Decoration Engineering NSTL,2022

3. Renewables Drive Backlog of Grid-Connection Projects[J];Laezman;Electrical contractor,2023

4. Cascading failure model in aviation network considering overload condition and failure probability[J];FAN;Journal of Computer Applications,2022

5. Stress Distribution in Direct Shear Loading and its Implication for Engineering Failure Analysis

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