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This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.
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