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- W3007309413 abstract "Precisely detecting the fault location on transmission lines can significantly save labor effort and accelerate the repairing and restoration process. This paper presents a novel single-ended fault location approach for transmission lines using modern deep learning techniques. A mixed convolutional neural network with long short-term memory (LSTM) structure are trained to predict the fault distance given the single-ended voltage and current measurements. Convolutional function, pooling layers, and the LSTM structure are used to preserve the translation invariance and capture the temporal correlation of the time-series input data. Advanced deep learning techniques such as adaptive moment estimation and dropout are used to efficiently train the neural network and prevent over-fitting. Extensive studies have demonstrated the accuracy and effectiveness of the deep-learning-based, singled-ended fault location approach." @default.
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- W3007309413 date "2019-10-01" @default.
- W3007309413 modified "2023-09-28" @default.
- W3007309413 title "Transmission Line Fault Location Using Deep Learning Techniques" @default.
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- W3007309413 doi "https://doi.org/10.1109/naps46351.2019.9000224" @default.
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