Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

BPNN topology diagram.

More »

Fig 1 Expand

Fig 2.

Flow chart of the algorithm structure of this paper.

More »

Fig 2 Expand

Table 1.

BFO-PSO-VMD-R-LMS-BPNN detection pseudocodes.

More »

Table 1 Expand

Fig 3.

Freiberg plus human equivalent circuit model.

More »

Fig 3 Expand

Table 2.

Experimental setup parameters.

More »

Table 2 Expand

Fig 4.

Iterative plots of k, ɑ, envelope entropy.

More »

Fig 4 Expand

Fig 5.

IMFs component.

More »

Fig 5 Expand

Fig 6.

(a) Simulated signal (b) Reconstructed signal.

More »

Fig 6 Expand

Fig 7.

BPNN detection and prediction.

(a) Actual and detected values (b) Actual and predicted values.

More »

Fig 7 Expand

Table 3.

Comparison of the detection results.

More »

Table 3 Expand

Fig 8.

Residual current real measurements for different types.

(a) Grass (b) Poplar (c) Wetland (d) Concrete.

More »

Fig 8 Expand

Fig 9.

Iterative plots of k, ɑ, envelope entropy.

More »

Fig 9 Expand

Fig 10.

IMFs component.

More »

Fig 10 Expand

Fig 11.

Comparison of the original simulated signal and the noise reduction signal.

(a) Original signal (b) Noise reduction signal.

More »

Fig 11 Expand

Fig 12.

LMS noise reduction signal (a) Poplar tree (b) Grass (c) Concrete ground.

More »

Fig 12 Expand

Fig 13.

BPNN plant electrocution current detection.

(a) Actual and detected values (b) Actual and predicted values.

More »

Fig 13 Expand

Fig 14.

Single-phase direct grounding of BPNN line.

(a) Actual and detected values (b) Actual and predicted values.

More »

Fig 14 Expand

Table 4.

Comparison of the detection results.

More »

Table 4 Expand

Table 5.

Kolmogorov-Smirnov test data analysis table.

More »

Table 5 Expand