Power distribution network fault diagnosis method based on deep feature clustering and LSTM
A distribution network fault and deep feature technology, which is applied in character and pattern recognition, biological neural network models, instruments, etc., can solve problems such as data imbalance, difficult training, and tediousness, so as to reduce negative impacts, avoid loss, and improve The effect of accuracy
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[0049] The accompanying drawings are for illustrative purposes only, and should not be construed as limiting the present invention; in order to better illustrate this embodiment, certain components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The positional relationship described in the drawings is for illustrative purposes only, and should not be construed as limiting the present invention.
[0050] Such as figure 1 As shown, a distribution network fault diagnosis method based on deep feature clustering and LSTM includes the following steps:
[0051] S1. Obtain typical data sets under various faults in the distribution network to form a fault sample data set, and divide the fault sample data set into a labeled sample set and an unlabeled sample set according to whe...
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