AliAmini93 / Fault-Detection-in-DC-microgrids

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

Date Created 2023-09-03 (about a year ago)

Commits 33 (last one 3 months ago)

Stargazers 35 (0 this week)

Watchers 1 (0 this week)

Forks 1

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RepositoryStats indexes 594,458 repositories, of these AliAmini93/Fault-Detection-in-DC-microgrids is ranked #544,870 (8th percentile) for total stargazers, and #543,556 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #15,346/17,480.

AliAmini93/Fault-Detection-in-DC-microgrids is also tagged with popular topics, for these it's ranked: neural-network (#1,059/1104), cnn (#520/554)

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cnn dwt fft svm digsilent random-forest anova-analysis neural-network data-augmentation feature-selection mutual-information xgboost-classifier pearson-correlation grid-search-hyperparameters linear-discriminant-analysis

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33 commits on the default branch (main) since jan '22

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The only known language in this repository is Jupyter Notebook

updated: 2024-12-07 @ 01:15pm, id: 686518120 / R_kgDOKOtvaA