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- W4285103613 abstract "This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ∼13 m in the fault location task." @default.
- W4285103613 created "2022-07-14" @default.
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- W4285103613 date "2022-04-24" @default.
- W4285103613 modified "2023-10-06" @default.
- W4285103613 title "An Algorithm for Fast Fault Location and Classification Based on Mathematical Morphology and Machine Learning" @default.
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- W4285103613 doi "https://doi.org/10.1109/isgt50606.2022.9817473" @default.
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