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Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
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Identification of composite power quality disturbances based on relative position matrix
inferred← citeshttps://doi.org/10.3389/fenrg.2024.1363028openalex-snapshot
Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems
inferred← citeshttps://doi.org/10.3390/electronics9111909openalex-snapshot
Compressed-Sensing-Based Time–Frequency Representation for Disturbance Characterization of Maglev On-Board Distribution Systems
inferred← citeshttps://doi.org/10.3390/en14154446openalex-snapshot
Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/en15041283openalex-snapshot
A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
inferred← citeshttps://doi.org/10.3390/en15072532openalex-snapshot
Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
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/en16114406openalex-snapshot
A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis
inferred← citeshttps://doi.org/10.3390/fractalfract7020159openalex-snapshot
An Interplay of Wigner–Ville Distribution and 2D Hyper-Complex Quadratic-Phase Fourier Transform
inferred← citeshttps://doi.org/10.3390/pr11071996openalex-snapshot
A Fault Diagnosis Method for Drilling Pump Fluid Ends Based on Time–Frequency Transforms
inferred← citeshttps://doi.org/10.3390/s21010154openalex-snapshot
Faulty Feeder Identification Based on Data Analysis and Similarity Comparison for Flexible Grounding System in Electric Distribution Networks
inferred← citeshttps://doi.org/10.3390/s26123955openalex-snapshot
Upper Limb Tremors Classification for Parkinson’s Disease Using W-Band (76–81 GHz) Doppler Millimeter-Wave Sensing and Deep-Learning-Based Classifier
inferred← citeshttps://doi.org/10.35290/ro.v4n3.2023.953openalex-snapshot
Diagnosis of the power quality in the welding warehouse of the company CIAUTO Cia. Ltda.
inferred← citeshttps://doi.org/10.36227/techrxiv.171177312.27398605/v1openalex-snapshot
On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach
inferred← citeshttps://doi.org/10.3934/energy.2025039openalex-snapshot
Detection and classification of power quality disturbances: Vision transformers vs. CNN
inferred← citeshttps://doi.org/10.54097/1b1mxy54openalex-snapshot
Research on the Impact of Power Electronics in New Distribution Networks
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