LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System-Reference-Cited by-同舟云学术

LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System

Published:2021 Issue: Volume:9 Page:32672-32687
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Veerasamy Veerapandiyan,Wahab Noor Izzri Abdul,Othman Mohammad Lutfi,Padmanaban Sanjeevikumar,Sekar Kavaskar,Ramachandran Rajeswari,Hizam Hashim,Vinayagam Arangarajan,Islam Mohammad Zohrul

Funder

Geran Putra Berimpak -University Putra Malaysia (GPB-UPM), Malaysia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09359758.pdf?arnumber=9359758

Cited by 117 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems;CAAI Artificial Intelligence Research;2024-12

2. Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives;Renewable and Sustainable Energy Reviews;2024-11

3. Deep learning approaches for visual faults diagnosis of photovoltaic systems: State-of-the-Art review;Results in Engineering;2024-09

4. An innovative transformer neural network for fault detection and classification for photovoltaic modules;Energy Conversion and Management;2024-08

5. Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring;Energy Conversion and Management;2024-08