Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering

Figure 8

The 3-dimensional results of different datasets for the training dataset through eight hidden layers by using an SDAE with PCA dimension reduction; 1–8 denote the hidden layer number. (a) SDAE-A-512-training data. (b) SDAE-B-512-training data. (c) SDAE-A-256-training data. (d) SDAE-B-256-training data. (e) SDAE-A-128-training data. (f) SDAE-B-128-training data. (g) SDAE-A-64-training data. (h) SDAE-B-64-training data. (i) SDAE-A-32-training data. (j) SDAE-B-32-training data. (k) SDAE-A-16-training data. (l) SDAE-B-16-training data. (m) SDAE-A-8-training data. (n) SDAE-B-8-training data. (o) SDAE-A-3-training data. (p) SDAE-B-3-training data.

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