An Automatic Identification Framework for Complex Power Quality Disturbances Based on Ensemble CNN-Reference-Cited by-同舟云学术

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An Automatic Identification Framework for Complex Power Quality Disturbances Based on Ensemble CNN

Published:2023 Issue: Volume:11 Page:56550-56560
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Minghao¹^ORCID,Deng Zhuofu¹^ORCID,Zhang Yuwei¹,Zhu Zhiliang¹^ORCID

Affiliation:

1. Software College, Northeastern University, Shenyang, China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10144761.pdf?arnumber=10144761

Reference34 articles.

1. An implementation of S-transform and type-2 fuzzy kernel based support vector machine algorithm for power quality events classification

2. Short-time Fourier transforms and Wigner-Ville distributions applied to the calibration of power frequency harmonic analyzers

3. Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

4. Non-stationary power signal processing for pattern recognition using HS-transform

5. An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances

Cited by 3 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. Classification Strategy for Power Quality Disturbances Based on Variational Mode Decomposition Algorithm and Improved Support Vector Machine;Processes;2024-05-25

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

3. High-Precision Intelligent Identification of Complex Power Quality Disturbances Based on Improved KST and CNNs;IEEE Access;2024

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