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AliAmini93 / Fault-Detection-in-DC-microgrids

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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.

neural-network random-forest svm cnn feature-selection fft dwt data-augmentation mutual-information linear-discriminant-analysis pearson-correlation grid-search-hyperparameters xgboost-classifier anova-analysis digsilent
  • Updated Aug 25, 2024
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