An Integrated Approach Improved Fast S-transform and SVD Noise Reduction for Classification of Power Quality Disruptions in Noisy Environments-Reference-Cited by-同舟云学术

An Integrated Approach Improved Fast S-transform and SVD Noise Reduction for Classification of Power Quality Disruptions in Noisy Environments

Published:2022-09-14 Issue:14-15 Volume:50 Page:868-885
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Goh Hui Hwang¹^ORCID,Liao Ling¹,Zhang Dongdong¹,Dai Wei¹,Lim Chee Shen²,Kurniawan Tonni Agustiono³,Goh Kai Chen⁴

Affiliation:

1. School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China

2. Department of Electrical and Electronic Engineering, School of Electrical Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

3. College of Environment and Ecology, Xiamen University, Xiamen, Fujian, China

4. Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

Funder

Guangxi University JunWu Scholar Research

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2022.2141928

Reference47 articles.

1. Data reduction of power quality disturbances—a wavelet transform approach

2. Power quality and the un-decimated wavelet transform: An analytic approach for time-varying disturbances

3. Detection and classification of power quality disturbances using S-transform and modular neural network

4. A method based on independent component analysis for single and multiple power quality disturbance classification

5. Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

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1. A Method for Extracting Weak Signals from Power Grid Based on Two-dimensional Singular Value Transformation;2023 4th International Conference on Advanced Electrical and Energy Systems (AEES);2023-12-01

2. Power quality recognition in noisy environment employing deep feature extraction from cross stockwell spectrum time–frequency images;Electrical Engineering;2023-09-02