Power quality recognition in noisy environment employing deep feature extraction from cross stockwell spectrum time–frequency images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-023-01995-0.pdf
Reference45 articles.
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2. Ekici S, Ucar F, Dandil B (2021) Power quality event classification using optimized Bayesian convolutional neural networks. Electr Eng 103:67–77
3. Borges FAS, Fernandes RAS, Silva IN, Silva CBS (2016) Feature extraction and power quality disturbances classification using smart meters signals. IEEE Trans Industr Inf 12(2):824–833
4. Akbarpour A, Nafar M, Simab M (2022) Multiple power quality disturbances detection and classification with fluctuations of amplitude and decision tree algorithm. Electr Eng 104:2333–2343
5. Zhong T, Zhang S, Cai G, Huang N (2018) Power-quality disturbance recognition based on time-frequency analysis and decision tree. IET Gener Transm Distrib 12(18):4153–4162
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. Deep Learning Aided Power Quality Disturbance Detection with Improved Time-frequency Resolution Employing Adaptive Superlet Transform;2024-08-10
2. Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm;Sensors;2024-04-12
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