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

Figure 7

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

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