Open Access

Design and Implementation of Adaptive Protection Strategy for 0.4kV Safety Anti Electric Shock Maintenance Power Box in Smart Grid

, , ,  and   
Mar 17, 2025

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Figure 1.

Repair power box security identification framework diagram
Repair power box security identification framework diagram

Figure 2.

The working principle diagram
The working principle diagram

Figure 3.

Safety recognition accuracy of different sample sizes
Safety recognition accuracy of different sample sizes

Figure 4.

Comparison of safety identification methods
Comparison of safety identification methods

Recognition results of inexperienced faces

Methods Accuracy Miss rate Detection rate
Eigenfaces 81.2% 18.8% 95.7%
Fisherfaces 75.8% 24.2% 96.2%
LBPH 82.7% 17.3% 96.5%
Our method 95.3% 4.7% 98.9%

Face recognition results

Face rotation Angle Our model(%) LLR(%) Gaussian regression(%)
25° 99.92 97.36 98.73
40° 97.64 89.83 90.27
55° 93.41 85.25 88.60
70° 90.11 82.53 87.37

The comparison of sample recognition accuracy and time consuming

Subject Method/sample size 1000 2000 3000 4000 5000
Accuracy (%) Code scanning 97.82 98.21 98.11 97.69 97.62
Ours 99.03 99.03 98.88 98.73 98.93
Time (ms) Code scanning 142 655 1132 2077 3352
Ours 38 55 72 80 87

Safety identification results

Sample size/group Accuracy(%)
Our method PCA-SVM LBP PCA-LDA-SVM LBP+MB-LBP LBP+LDRC-Fisher
500 99.23 97.37 96.15 92.53 90.62 88.72
1000 99.03 97.56 97.51 92.38 90.38 88.72
2000 99.03 97.41 96.93 92.87 89.50 89.98
3000 98.88 97.22 96.54 91.79 88.52 88.03
4000 98.73 97.61 97.47 92.09 89.01 89.40
5000 98.93 96.83 96.34 91.79 89.30 87.74
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