Multi‐task learning method for classification of multiple power quality disturbances-Reference-Cited by-同舟云学术

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Multi‐task learning method for classification of multiple power quality disturbances

Published:2020-01-31 Issue:5 Volume:14 Page:900-909
ISSN:1751-8695
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Dong Youli¹^ORCID,Cao Hanqiang¹,Ding Xiaojun²,Xu Guoping¹,Yue Chunyi¹

Affiliation:

1. School of Electronic Information and Communications, Huazhong University of Science and TechnologyLuoyu Road 1037WuhanPeople's Republic of China

2. School of Electrical and Electronic Engineering, Huazhong University of Science and TechnologyLuoyu Road 1037WuhanPeople's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2019.0812

Reference37 articles.

1. Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review

2. Comparison between discrete STFT and wavelets for the analysis of power quality events;Jurado F.;Electr. Power Syst. Res.,2002

3. Automatic classification of power quality events and disturbances using wavelet transform and support vector machines

4. Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm

5. Modified S transform and ELM algorithms and their applications in power quality analysis

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1. Power quality monitoring in electric grid integrating offshore wind energy: A review;Renewable and Sustainable Energy Reviews;2024-03

2. Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set;IET Generation, Transmission & Distribution;2023-01

3. Voltage Sag Classification Based on Multi-task Parallel Convolutional Neural Network;IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society;2022-10-17

4. Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine;IET Generation, Transmission & Distribution;2022-02-08

5. Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN;IET Generation, Transmission & Distribution;2021-12-06

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