Intelligent substation process layer network abnormal flow detection method based on deep learning
The invention discloses an intelligent substation process level network abnormal flow detection method based on deep learning, which comprises the following steps: collecting substation process level network time sequence flow data under different operation conditions, respectively extracting time d...
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Main Authors | , |
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Format | Patent |
Language | Chinese English |
Published |
22.07.2022
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Subjects | |
Online Access | Get full text |
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Summary: | The invention discloses an intelligent substation process level network abnormal flow detection method based on deep learning, which comprises the following steps: collecting substation process level network time sequence flow data under different operation conditions, respectively extracting time domain and time-frequency domain features, carrying out normalization processing, and then calculating the abnormal flow of the substation process level network according to the extracted time domain and time-frequency domain features; and obtaining sample data comprising a sampling moment, a sample type, a time domain feature and a time-frequency domain feature. Training the constructed LSTM neural network by using sample data, and determining structural parameters and hyper-parameters of the model; the dimensionality of the model output vector is the network flow type number of the substation process layer. And finally, detecting the network flow data of the process layer of the transformer substation by adopting |
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Bibliography: | Application Number: CN202210357838 |