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https://doi.org/10.2139/ssrn.4164374

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.asoc.2015.10.038openalex-extracted
Combined VMD-SVM based feature selection method for classification of power quality events
inferredcites →https://doi.org/10.1016/j.dsp.2013.02.012openalex-extracted
Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier
inferredcites →https://doi.org/10.1016/j.neucom.2011.06.041openalex-extracted
Power quality disturbances classification based on S-transform and probabilistic neural network
inferredcites →https://doi.org/10.1049/gtd2.12364openalex-extracted
Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN
inferredcites →https://doi.org/10.1049/iet-gtd.2017.0897openalex-extracted
Fault location based on travelling wave identification using an adaptive extended Kalman filter
inferredcites →https://doi.org/10.1049/iet-gtd.2019.1121openalex-extracted
Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance
inferredcites →https://doi.org/10.1049/iet-gtd.2019.1678openalex-extracted
Classification of power quality disturbances based on KF‐ML‐aided S‐transform and multilayers feedforward neural networks
inferredcites →https://doi.org/10.1049/iet-spr.2017.0486openalex-extracted
Double‐ended travelling‐wave fault location based on residual analysis using an adaptive EKF
inferredcites →https://doi.org/10.1109/access.2019.2937193openalex-extracted
Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/access.2020.2993202openalex-extracted
A New Convolutional Network Structure for Power Quality Disturbance Identification and Classification in Micro-Grids
inferredcites →https://doi.org/10.1109/eeeic.2019.8783378openalex-extracted
Detection and Classification of Multiple Power Quality Disturbances based on Temporal Deep Learning
inferredcites →https://doi.org/10.1109/tia.2014.2356639openalex-extracted
Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree
inferredcites →https://doi.org/10.1109/tii.2015.2486379openalex-extracted
Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
inferredcites →https://doi.org/10.1109/tii.2018.2871253openalex-extracted
Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition
inferredcites →https://doi.org/10.1109/tii.2019.2895054openalex-extracted
A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance
inferredcites →https://doi.org/10.1109/tim.2016.2578518openalex-extracted
Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs
inferredcites →https://doi.org/10.1109/tsg.2015.2480065openalex-extracted
A Novel Method of Fault Location for Single-Phase Microgrids
inferredcites →https://doi.org/10.1109/tsg.2016.2624313openalex-extracted
Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances
inferredcites →https://doi.org/10.1109/upec.2015.7339928openalex-extracted
Evaluation and classification of power quality disturbances based on discrete Wavelet Transform and artificial neural networks

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inferred← citeshttps://doi.org/10.3389/fphy.2025.1616367openalex-snapshot
Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification
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