Improving Power Quality measurements using deep learning for disturbance classification-Reference-Cited by-同舟云学术

Improving Power Quality measurements using deep learning for disturbance classification

Published:2023-05-22 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
language:
Short-container-title:

Author:

Patrizi Gabriele¹,Iturrino-García Carlos¹,Bartolini Alessandro¹,Ermini Francesco¹,Paolucci Libero¹,Ciani Lorenzo¹,Grasso Francesco¹,Catelani Marcantonio¹

Affiliation:

1. University of Florence,Department of Information Engineering,Florence,Italy,50139

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10175865/10175876/10176109.pdf?arnumber=10176109

Reference25 articles.

1. Review on detection and classification of underlying causes of power quality disturbances using signal processing and soft computing technique

2. Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

3. A Two-Stage Wavelet Decomposition Method for Instantaneous Power Quality Indices Estimation Considering Interharmonics and Transient Disturbances

4. Detection of Short-Term Voltage Disturbances and Harmonics Using μPMU-Based Variational Mode Extraction Method

5. Detection and Classification of Power Quality Disturbances Using Time-Frequency Analysis Technique

Cited by 2 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. Anomaly Detection for Power Quality Analysis Using Smart Metering Systems;Sensors;2024-09-06

2. A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion;IEEE Access;2024