相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。High-Precision Identification of Power Quality Disturbances Under Strong Noise Environment Based on FastICA and Random Forest
Jun Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)
FPGA-Based Deep Convolutional Neural Network of Process Adaptive VMD Data With Online Sequential RVFLN for Power Quality Events Recognition
Mrutyunjaya Sahani et al.
IEEE TRANSACTIONS ON POWER ELECTRONICS (2021)
Classification and Visualization of Power Quality Disturbance-Events Using Space Vector Ellipse in Complex Plane
Mollah Rezaul Alam et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2021)
Classification of power quality disturbances based on KF-ML-aided S-transform and multilayers feedforward neural networks
Yanhui Xi et al.
IET GENERATION TRANSMISSION & DISTRIBUTION (2020)
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks
James J. Q. Yu et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM
Karthik Thirumala et al.
NEUROCOMPUTING (2019)
Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review
Manohar Mishra
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2019)
Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
Kewei Cai et al.
IEEE ACCESS (2019)
Complex power quality disturbances classification via curvelet transform and deep learning
Hui Liu et al.
ELECTRIC POWER SYSTEMS RESEARCH (2018)
Power quality assessment and event detection in hybrid power system
Abdul Gafoor Shaik et al.
ELECTRIC POWER SYSTEMS RESEARCH (2018)
Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine
Mrutyunjaya Sahani et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Variational mode decomposition and weighted online sequential extreme learning machine for power quality event patterns recognition
Mrutyunjaya Sahani et al.
NEUROCOMPUTING (2018)
Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System
Pankaj D. Achlerkar et al.
IEEE TRANSACTIONS ON SMART GRID (2018)
Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples
Zhipeng Feng et al.
MEASUREMENT (2017)
Power quality disturbance detection and classification using wavelet and RBFNN
P. Kanirajan et al.
APPLIED SOFT COMPUTING (2015)
An effective Power Quality classifier using Wavelet Transform and Support Vector Machines
D. De Yong et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Integrated DWT-FFT approach for detection and classification of power quality disturbances
S. A. Deokar et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2014)
The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends
Michel P. Tcheou et al.
IEEE TRANSACTIONS ON SMART GRID (2014)
Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster-Shafer algorithm
H. Dehghani et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2013)
Characterizing and monitoring voltage transients as problem to sensitive loads
Hugo Ribeiro et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2012)
Mixed Over-Voltage Decomposition Using Atomic Decompositions Based on a Damped Sinusoids Atom Dictionary
Qing Yang et al.
ENERGIES (2011)
Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation
Silong Peng et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING (2010)
Detection and classification of power quality disturbances using S-transform and probabilistic neural network
S. Mishra et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2008)
Efficient coherent adaptive representations of monitored electric signals in power systems using damped sinusoids.
L Lovisolo et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING (2005)