Hong-Wei Xu
ABSTRACT
How to find the abnormal points in data effectively and quickly and give a reasonable explanation is the main content of anomaly detection. The development of deep learning technology provides a new idea for the abnormal analysis and processing of voltage test data. This paper applies deep learning theory to the abnormal analysis and processing of voltage test data, and puts forward a model for the abnormal analysis and processing of voltage test data based on deep learning. Based on 3D CNN (convolutional neural network), the constructed time series voltage test data are classified, evaluated and analyzed. In this paper, the output of 3D CNN is as close as possible to the input, the voltage test data is taken as the input, the minimum reconstruction error is taken as the tuning standard in the training stage, and the network output is the voltage reconstruction data corresponding to the input. The research results show that the hidden levels 1, 2, 3 and 4 all show good classification accuracy, all reaching more than 90%. The proposed algorithm does perform well in different outlier ratios and has good robustness.
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Index Terms
- Abnormal Analysis and Treatment of Voltage Test Data Based on Deep Learning
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