HIGHLIGHTS
SUMMARY
The main contribution of this work is the development of an anomaly detection algorithm based on a neural_network autoencoder. Ref shows a contextual anomaly detection method based on an artificial neural_network and explains the use of this method to discover voltage control manipulation in the low voltage distribution grid. A deep learning scheme composed of Long Short Term Memory-Stacked Autoencoders and Convolutional Neural_Network (CNN-SAE) followed by a softmax activation layer has been used for fault detection in a wind turbine in Ref. Three different autoencoding schemes (multilayer perceptron, convolutional, and long-short . . .
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