Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine-Reference-Cited by-同舟云学术

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Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine

Published:2022-02-08 Issue:10 Volume:16 Page:1974-1989
ISSN:1751-8687
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation Trans & Dist

Author:

Wu Jianzhang¹^ORCID,Mei Fei²,Zhang Chenyu³,Miao Huiyu³,Li Kai¹,Zheng Jianyong¹⁴

Affiliation:

1. School of Electrical Engineering Southeast University Nanjing People's Republic of China

2. College of Energy and Electrical Engineering Hohai University Nanjing People's Republic of China

3. Research Institute State Grid Jiangsu Electric Power Co., Ltd. Nanjing People's Republic of China

4. Suzhou Research Institute of Southeast University Suzhou People's Republic 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/gtd2.12407

Reference41 articles.

1. Real‐time cross‐correlation‐based technique for detection and classification of power quality disturbances

2. Time–frequency–scale transform for analysis of PQ disturbances

3. A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network

4. Power‐quality disturbance recognition based on time‐frequency analysis and decision tree

5. FPGA-Based Online Power Quality Disturbances Monitoring Using Reduced-Sample HHT and Class-Specific Weighted RVFLN

Cited by 4 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. Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier;IEEE Access;2024

2. Power Quality Disturbances Segmentation: An Approach Based on Gramian Angular Field;Electric Power Components and Systems;2023-09-25

3. Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method;Frontiers in Energy Research;2023-01-09

4. A Generalized Classification Framework for Power Quality Disturbances Based on Synchrosqueezed Wavelet Transform and Convolutional Neural Networks;IEEE Transactions on Instrumentation and Measurement;2023

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