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Enhancing Methodology for Fault Detection and Classification via Embeddings Considering PV System Insertion
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/978-1-4419-9982-5openalex-snapshot
Introduction to Smooth Manifolds
inferredcites →https://doi.org/10.1016/j.compeleceng.2018.05.025openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.epsr.2011.02.013openalex-snapshot
Fault diagnosis in distribution networks with distributed generation
inferredcites →https://doi.org/10.1016/j.epsr.2015.10.008openalex-snapshot
Single-phasing detection and classification in distribution systems with a high penetration of distributed generation
inferredcites →https://doi.org/10.1016/j.neunet.2019.04.015openalex-snapshot
Unsupervised feature selection via latent representation learning and manifold regularization
inferredcites →https://doi.org/10.1016/j.procs.2018.05.198openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.renene.2014.03.035openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rser.2019.01.050openalex-snapshot
Impact of distributed generation on protection and voltage regulation of distribution systems: A review
inferredcites →https://doi.org/10.1016/j.solener.2020.06.097openalex-snapshot
Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Tree Induction for Probability-Based Ranking
inferredcites →https://doi.org/10.1038/s41467-019-13056-xopenalex-snapshot
The art of using t-SNE for single-cell transcriptomics
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Initialization is critical for preserving global data structure in both t-SNE and UMAP
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Impact of distributed generation on the protection systems of distribution networks: analysis and remedies – review paper
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Variations in Variational Autoencoders - A Comparative Evaluation
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Fault Classification in Distribution Systems Using Deep Learning With Data Preprocessing Methods Based on Fast Dynamic Time Warping and Short-Time Fourier Transform
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Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM
inferredcites →https://doi.org/10.1109/icdm.2001.989510openalex-snapshot
Using rule sets to maximize ROC performance
inferredcites →https://doi.org/10.1109/jsen.2019.2913006openalex-snapshot
Deep-Learning-Based Fault Classification Using Hilbert–Huang Transform and Convolutional Neural Network in Power Distribution Systems
inferredcites →https://doi.org/10.1109/pesgm40551.2019.8973961openalex-snapshot
An S-Transform based approach for fault detection and classification in power distribution systems
inferredcites →https://doi.org/10.1109/powercon.2010.5666589openalex-snapshot
Impedance-based fault location formulation for unbalanced primary distribution systems with distributed generation
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Deep Manifold Structure Transfer for Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2009.187openalex-snapshot
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1109/tsg.2012.2190759openalex-snapshot
Accommodating High PV Penetration on Distribution Feeders
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1155/2023/4418741openalex-snapshot
Fault Detection and Classification to Design a Protection Scheme for Utility Grid with High Penetration of Wind and Solar Energy
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Convolutional Neural Networks: A Survey
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Investigating Small-Scale DER Impact on Fault Currents and Overcurrent Protection Coordination in Distribution Feeders Under Brazilian Technical Standards
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Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms
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