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Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree

https://doi.org/10.1109/tii.2012.2210230
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Fast Methods for Power Quality Analysis
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UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY
inferred← citeshttps://doi.org/10.2174/1874129001409010208openalex-snapshot
Power Quality Disturbance Location Method based on Cross-Feedback - Recursive Least Squares
inferred← citeshttps://doi.org/10.23919/measurement.2017.7983524openalex-snapshot
Power quality disturbance classification based on wavelet transform and support vector machine
inferred← citeshttps://doi.org/10.23919/splitech.2019.8782985openalex-snapshot
A Methodology for Detection of Power Quality Disturbances in the Context of Demand Side Management
inferred← citeshttps://doi.org/10.3233/ifs-131103openalex-snapshot
Nonstationary signal pattern recognition using fast time-time filtering and decision tree
inferred← citeshttps://doi.org/10.3233/jifs-212399openalex-snapshot
Combined ST/MST and radial basis function neural networks for power quality disturbance signal classification
inferred← citeshttps://doi.org/10.3233/kes-190414openalex-snapshot
A low rank robust kernel ridge regression classifier for power quality disturbance pattern recognition
inferred← citeshttps://doi.org/10.3390/app11167637openalex-snapshot
Automatic Power Quality Disturbance Diagnosis Based on Residual Denoising Convolutional Auto-Encoder
inferred← citeshttps://doi.org/10.3390/app9112228openalex-snapshot
Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis
inferred← citeshttps://doi.org/10.3390/app9183681openalex-snapshot
Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/electronics10212725openalex-snapshot
Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
inferred← citeshttps://doi.org/10.3390/en11113040openalex-snapshot
Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network
inferred← citeshttps://doi.org/10.3390/en13112761openalex-snapshot
Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study
inferred← citeshttps://doi.org/10.3390/en16062685openalex-snapshot
Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/math10213949openalex-snapshot
Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
inferred← citeshttps://doi.org/10.3390/s21165544openalex-snapshot
Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System
inferred← citeshttps://doi.org/10.4304/jnw.9.11.2987-2993openalex-snapshot
A Cloud Service Resource Classification Strategy Based on Feature Similarity
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Research and Implementation of a USB Interfaced Real-Time Power Quality Disturbance Classification System
inferred← citeshttps://doi.org/10.46604/peti.2024.13831openalex-snapshot
Efficient Classification of Power Quality Using Long Short-Term Memory Network Technique
inferred← citeshttps://doi.org/10.48084/etasr.2409openalex-snapshot
Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances
inferred← citeshttps://doi.org/10.70675/a048ac00z1754z418fza45ez902b87f9e5bfopenalex-snapshot
On electric grid power quality monitoring using parametric signal processing techniques
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