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

https://doi.org/10.1109/tii.2019.2892873
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/etep.2597openalex-snapshot
A new embedded power quality event classification system based on the wavelet transform
inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
inferredcites →https://doi.org/10.1016/j.epsr.2012.09.007openalex-snapshot
A new classification for power quality events in distribution systems
inferredcites →https://doi.org/10.1016/j.epsr.2016.01.003openalex-snapshot
A rule-based S-Transform and AdaBoost based approach for power quality assessment
inferredcites →https://doi.org/10.1016/j.epsr.2016.09.003openalex-snapshot
A novel methodology for modeling waveforms for power quality disturbance analysis
inferredcites →https://doi.org/10.1016/j.measurement.2018.02.058openalex-snapshot
FPGA-based online power quality monitoring system for electrical distribution network
inferredcites →https://doi.org/10.1023/a:1018628609742openalex-snapshot
Least Squares Support Vector Machine Classifiers
inferredcites →https://doi.org/10.1098/rspa.1998.0193openalex-snapshot
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
inferredcites →https://doi.org/10.1109/72.471375openalex-snapshot
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
inferredcites →https://doi.org/10.1109/72.737490openalex-snapshot
The ensemble approach to neural-network learning and generalization
inferredcites →https://doi.org/10.1109/tcyb.2016.2588526openalex-snapshot
Visual Tracking With Convolutional Random Vector Functional Link Network
inferredcites →https://doi.org/10.1109/tia.2014.2356639openalex-snapshot
Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree
inferredcites →https://doi.org/10.1109/tie.2008.928111openalex-snapshot
Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization
inferredcites →https://doi.org/10.1109/tie.2013.2248335openalex-snapshot
Automatic Classification of Power Quality Events Using Balanced Neural Tree
inferredcites →https://doi.org/10.1109/tie.2015.2499728openalex-snapshot
Identification of Power Disturbances Using Generalized Morphological Open-closing and Close-opening Undecimated Wavelet
inferredcites →https://doi.org/10.1109/tie.2016.2521615openalex-snapshot
Disturbance Ratio for Optimal Multi-Event Classification in Power Distribution Networks
inferredcites →https://doi.org/10.1109/tii.2012.2210230openalex-snapshot
Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree
inferredcites →https://doi.org/10.1109/tii.2013.2289392openalex-snapshot
Power Quality Event Classification Under Noisy Conditions Using EMD-Based De-Noising Techniques
inferredcites →https://doi.org/10.1109/tii.2015.2486379openalex-snapshot
Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
inferredcites →https://doi.org/10.1109/tii.2017.2772081openalex-snapshot
A New Approach to Power System Disturbance Assessment Using Wide-Area Postdisturbance Records
inferredcites →https://doi.org/10.1109/tim.2013.2258761openalex-snapshot
A Real-Time Power Quality Disturbances Classification Using Hybrid Method Based on S-Transform and Dynamics
inferredcites →https://doi.org/10.1109/tim.2016.2578518openalex-snapshot
Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs
inferredcites →https://doi.org/10.1109/tim.2017.2663560openalex-snapshot
Instantaneous Power Quality Indices Based on Single-Sideband Modulation and Wavelet Packet-Hilbert Transform
inferredcites →https://doi.org/10.1109/tim.2017.2761239openalex-snapshot
A Modified S-Transform and Random Forests-Based Power Quality Assessment Framework
inferredcites →https://doi.org/10.1109/tpwrd.2007.911125openalex-snapshot
Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network
inferredcites →https://doi.org/10.1109/tpwrd.2009.2028792openalex-snapshot
Empirical-Mode Decomposition With Hilbert Transform for Power-Quality Assessment
inferredcites →https://doi.org/10.1109/tsg.2015.2397431openalex-snapshot
A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM
inferredcites →https://doi.org/10.1109/tsmcb.2011.2168604openalex-snapshot
Extreme Learning Machine for Regression and Multiclass Classification
inferredcites →https://doi.org/10.1109/tsp.2011.2143711openalex-snapshot
Wavelet Transform With Tunable Q-Factor
inferredcites →https://doi.org/10.1109/tsp.2013.2265222openalex-snapshot
Empirical Wavelet Transform
inferredcites →https://doi.org/10.1142/imsopenalex-snapshot
Interdisciplinary Mathematical Sciences
inferredcites →https://doi.org/10.3390/e17020669openalex-snapshot
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

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inferred← citeshttps://doi.org/10.2139/ssrn.4484894openalex-extracted
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inferred← citeshttps://doi.org/10.1002/adts.202000118openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ett.70242openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/stc.2635openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-97-1447-6_15openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00202-021-01243-3openalex-snapshot
Feature extraction and power quality event classification using Curvelet transform and optimized extreme learning machine
inferred← citeshttps://doi.org/10.1007/s00202-022-01505-8openalex-snapshot
Fast TT transform and optimized probabilistic neural network-based power quality event detection and classification
inferred← citeshttps://doi.org/10.1007/s00202-023-01977-2openalex-snapshot
A novel microgrid islanding classification algorithm based on combining hybrid feature extraction approach with deep ResNet model
inferred← citeshttps://doi.org/10.1007/s00202-025-03149-wopenalex-snapshot
A comprehensive review of feature extraction techniques for power quality event detection with novel approaches for enhanced classification in smart grids
inferred← citeshttps://doi.org/10.1007/s00521-020-05282-yopenalex-snapshot
An optimal extreme learning-based classification method for power quality events using fractional Fourier transform
inferred← citeshttps://doi.org/10.1007/s10462-024-10759-6openalex-snapshot
A survey on imbalanced learning: latest research, applications and future directions
inferred← citeshttps://doi.org/10.1007/s10489-021-02664-0openalex-snapshot
Modified added activation function based exponential robust random vector functional link network with expanded version for nonlinear system identification
inferred← citeshttps://doi.org/10.1007/s10586-024-04875-9openalex-snapshot
Developing a two stage optimized random vector functional link neural network based predictor model utilizing a swift crow search algorithm
inferred← citeshttps://doi.org/10.1007/s11356-023-31656-5openalex-snapshot
Smart meter for the estimation of power quality indices based on second-order generalized integrator
inferred← citeshttps://doi.org/10.1007/s11760-021-01878-6openalex-snapshot
Weighted Least Square Design Technique for Hilbert Transformer using Fractional Derivative
inferred← citeshttps://doi.org/10.1007/s12530-023-09501-4openalex-snapshot
RVFLN-CDFPA: a random vector functional link neural network optimized using a chaotic differential flower pollination algorithm for day ahead Net Asset Value prediction
inferred← citeshttps://doi.org/10.1007/s13369-019-04289-5openalex-snapshot
Power Quality Events Recognition Using S-Transform and Wild Goat Optimization-Based Extreme Learning Machine
inferred← citeshttps://doi.org/10.1007/s40313-021-00886-8openalex-snapshot
Power Quality Parameters Calculation Using FPGA Embedded Parallel Processors in Compliance with the IEC 61000-4-30 Standard
inferred← citeshttps://doi.org/10.1016/b978-0-12-823346-7.00010-4openalex-snapshot
Recent trends and advances in power quality
inferred← citeshttps://doi.org/10.1016/j.asoc.2020.106117openalex-snapshot
Non linear system identification using kernel based exponentially extended random vector functional link network
inferred← citeshttps://doi.org/10.1016/j.asoc.2023.110377openalex-snapshot
Random vector functional link network: Recent developments, applications, and future directions
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109887openalex-snapshot
A robust variational mode decomposition based deep random vector functional link network for dynamic system identification
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110615openalex-snapshot
Detection and extraction of optimal features from power quality disturbances based on wavelet coefficient reconstruction and noise-robust methods
inferred← citeshttps://doi.org/10.1016/j.egyr.2023.05.105openalex-snapshot
Power quality disturbance detection based on IEWT
inferred← citeshttps://doi.org/10.1016/j.energy.2021.122585openalex-snapshot
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
inferred← citeshttps://doi.org/10.1016/j.epsr.2022.107866openalex-snapshot
FFNet: An automated identification framework for complex power quality disturbances
inferred← citeshttps://doi.org/10.1016/j.epsr.2022.108245openalex-snapshot
Time-varying voltage flicker analysis based on analytic-adaptive variational mode decomposition
inferred← citeshttps://doi.org/10.1016/j.epsr.2023.109897openalex-snapshot
Identification of power quality disturbance characteristic based on deep learning
inferred← citeshttps://doi.org/10.1016/j.epsr.2024.110567openalex-snapshot
Context-aware voltage sag waveform decomposition method using hybrid models for complex power quality detection
inferred← citeshttps://doi.org/10.1016/j.epsr.2025.112504openalex-snapshot
Multisynchrosqueezing transform aided transfer learning based approach for diagnosis of single and mixed power quality events
inferred← citeshttps://doi.org/10.1016/j.ijepes.2023.109476openalex-snapshot
The early detection of short-circuit fault in the active low-voltage AC system
inferred← citeshttps://doi.org/10.1016/j.rser.2020.110050openalex-snapshot
Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review
inferred← citeshttps://doi.org/10.1016/j.suscom.2020.100417openalex-snapshot
A critical and comprehensive review on power quality disturbance detection and classification
inferred← citeshttps://doi.org/10.1038/s41598-026-35376-xopenalex-snapshot
A hybrid AI framework for identification of power quality disturbances in electrical network
inferred← citeshttps://doi.org/10.1049/el.2020.1773openalex-snapshot
FPGA‐based parallel process for Walsh–Hadamard transform
inferred← citeshttps://doi.org/10.1049/gtd2.12407openalex-snapshot
Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine
inferred← citeshttps://doi.org/10.1049/iet-gtd.2019.1121openalex-snapshot
Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance
inferred← citeshttps://doi.org/10.1049/iet-gtd.2020.0131openalex-snapshot
Simplified and fast method without considering filter for voltage flicker detection
inferred← citeshttps://doi.org/10.1088/1742-6596/2814/1/012029openalex-snapshot
An automatic recognition method for power quality disturbances based on CAB-Net
inferred← citeshttps://doi.org/10.1109/access.2020.3014732openalex-snapshot
Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
inferred← citeshttps://doi.org/10.1109/access.2023.3306831openalex-snapshot
Voltage Sag Assessment, Detection, and Classification in Distribution Systems Embedded With Fast Charging Stations
inferred← citeshttps://doi.org/10.1109/access.2023.3326950openalex-snapshot
Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
inferred← citeshttps://doi.org/10.1109/access.2024.3406164openalex-snapshot
A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification
inferred← citeshttps://doi.org/10.1109/access.2025.3571623openalex-snapshot
A Smart Web-Based Power Quality and Energy Monitoring System With Enhanced Features
inferred← citeshttps://doi.org/10.1109/aees59800.2023.10468938openalex-snapshot
A Method for Extracting Weak Signals from Power Grid Based on Two-dimensional Singular Value Transformation
inferred← citeshttps://doi.org/10.1109/aisp53593.2022.9760627openalex-snapshot
Power Quality Assessment in a Microgrid System using Signal Decomposition Techniques
inferred← citeshttps://doi.org/10.1109/apsit52773.2021.9641064openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cce56709.2022.9975950openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ceepe64987.2025.11034165openalex-snapshot
Construction and Application of Transmission Panoramic Monitoring Platform
inferred← citeshttps://doi.org/10.1109/ciycee53554.2021.9676964openalex-snapshot
An Efficient Denoising Algorithm for Power Quality Disturbance in Fractional Domain
inferred← citeshttps://doi.org/10.1109/ecce-europe62795.2025.11239000openalex-snapshot
Machine Intelligence for Power Quality Event Recognition
inferred← citeshttps://doi.org/10.1109/i2ct51068.2021.9418004openalex-snapshot
Three phase power quality disturbance detection and classification by SCGB Neural Network
inferred← citeshttps://doi.org/10.1109/icpics58376.2023.10235628openalex-snapshot
Optimal Noise Reduction Smooth Model for Power Quality Disturbance Signals Based on VMD
inferred← citeshttps://doi.org/10.1109/ifeea60725.2023.10429208openalex-snapshot
Design of Anti-interference Partial Discharge Detection Device Based on HHT Algorithm
inferred← citeshttps://doi.org/10.1109/mepcon63025.2024.10850458openalex-snapshot
A Machine Learning-Based Robust Classifier for Power Quality Disturbances in Smart Grids
inferred← citeshttps://doi.org/10.1109/sefet57834.2023.10245639openalex-snapshot
Assessment of Transient Disturbance using Discrete Fourier Transform and Feed Forward Neural Network based Hybrid Classifier
inferred← citeshttps://doi.org/10.1109/southeastcon44009.2020.9249698openalex-snapshot
Automated Identification of Re-Closing Events in an Operational Smart Power Grid
inferred← citeshttps://doi.org/10.1109/spin48934.2020.9071150openalex-snapshot
DSP Implementation of Hilbert Transform for Voltage Variation Tracking
inferred← citeshttps://doi.org/10.1109/tai.2022.3162570openalex-snapshot
Multitarget Stochastic Configuration Network and Applications
inferred← citeshttps://doi.org/10.1109/tia.2020.2982850openalex-snapshot
Detection and Characterization of Time-variant Non-stationary Voltage Sag Waveforms Using Segmented Hilbert Huang Transform
inferred← citeshttps://doi.org/10.1109/tie.2019.2935978openalex-snapshot
AM Modulation Signal Estimation Allowing Further Research on Sources of Voltage Fluctuations
inferred← citeshttps://doi.org/10.1109/tii.2019.2945822openalex-snapshot
Automatic Power Quality Events Recognition Using Modes Decomposition Based Online P-Norm Adaptive Extreme Learning Machine
inferred← citeshttps://doi.org/10.1109/tii.2020.2966223openalex-snapshot
High-Precision Identification of Power Quality Disturbances Under Strong Noise Environment Based on FastICA and Random Forest
inferred← citeshttps://doi.org/10.1109/tii.2020.2968165openalex-snapshot
2019 Index IEEE Transactions on Industrial Informatics Vol. 15
inferred← citeshttps://doi.org/10.1109/tii.2020.2971709openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tii.2021.3069869openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tii.2021.3083240openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tii.2021.3132334openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tim.2021.3054673openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tim.2021.3107009openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tim.2026.3723494openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tnnls.2020.3027984openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tnnls.2024.3353531openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tsg.2020.2990079openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2021/6561730openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2022/8519379openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/acis/6466035openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/15501329221087055openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s41601-023-00277-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1364/jocn.585416openalex-snapshot
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inferred← citeshttps://doi.org/10.17559/tv-20230712000799openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.4847727openalex-snapshot
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inferred← citeshttps://doi.org/10.22373/ekw.v8i2.13026openalex-snapshot
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inferred← citeshttps://doi.org/10.2516/ogst/2020081openalex-snapshot
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inferred← citeshttps://doi.org/10.32604/ee.2023.025900openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en13112761openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en15051909openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en15072328openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en16010252openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en18154088openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/foods13203300openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/info16030180openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/math10213949openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/s24041121openalex-snapshot
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inferred← citeshttps://doi.org/10.34019/ufjf/di/2021/00013openalex-snapshot
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inferred← citeshttps://doi.org/10.36227/techrxiv.19518682openalex-snapshot
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inferred← citeshttps://doi.org/10.36227/techrxiv.19518682.v1openalex-snapshot
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