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Application of automation technology in power system protection and control

Lirong Xiao

Lirong Xiao

State Grid Nanchang Power Supply CompanyNanchang, China
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Xiao, Lirong
, 
Fugen Shu

Fugen Shu

State Grid Nanchang Power Supply CompanyNanchang, China
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Shu, Fugen
 e 
Taiping Wu

Taiping Wu

State Grid Jiangxi Electric Power Co., Ltd.Nanchang, China
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Wu, Taiping
  
29 set 2025
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Pubblicato online: 29 set 2025
Ricevuto: 26 gen 2025
Accettato: 10 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1131
Parole chiave
© 2025 Lirong Xiao, Fugen Shu and Taiping Wu, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Convolutional neural network structure
Convolutional neural network structure

Figure 2.

The case study representing sources at both line terminals and three lines
The case study representing sources at both line terminals and three lines

Figure 3.

The flow of power system protection scheme
The flow of power system protection scheme

Figure 4.

Hardware configuration diagram of PLC
Hardware configuration diagram of PLC

Figure 5.

The variation of two voltages when different faults occur
The variation of two voltages when different faults occur

Figure 6.

Change curve of current difference ID under different faults
Change curve of current difference ID under different faults

Figure 7.

ID Change curve under different fault resistances
ID Change curve under different fault resistances

Figure 8.

ID change curve under different flat wave reactors
ID change curve under different flat wave reactors

Figure 9.

The whole process of fault protection removal
The whole process of fault protection removal

Figure 10.

Voltage output waveform
Voltage output waveform

Figure 11.

The voltage output waveform is controlled by PLC
The voltage output waveform is controlled by PLC

Figure 12.

Comparison result of motor speed curve
Comparison result of motor speed curve

Training results under different parameters

No. First filter size N Second filter size Nr Number of iterations Training set error rate Verify the set error rate
1 10×10 12 10×10 32 60 0.5381% 0.5793%
2 10×10 20 10×10 32 60 0.4877% 0.5595%
3 10×10 12 10×10 20 60 0.6762% 0.8187%
4 10×10 12 10×10 32 40 0.5391% 0.6679%
5 10×10 12 10×10 32 10 5.0672% 9.4767%
6 6×6 12 10×10 32 60 0.4993% 0.5282%
7 18×18 12 10×10 32 40 0.5988% 0.6996%
8 10×10 12 6×6 20 40 0.6796% 0.7791%
9 6×6 20 10×10 32 40 0.4789% 0.4992%

Power system voltage waveform data

Type Before optimization Post-optimization Difference value Rate of change
Maximum value /KV 36.17 34.39 1.78 4.92%
Minimum value /KV -35.04 -34.84 0.2 0.57%
Valid value /KV 22.35 24.39 2.04 9.13%

Test result

Category NF 1PG 1NG 1PN 2PG 2NG 2PN 3PG 3NG 3PN Accuracy
NF 6000 0 0 0 0 0 0 0 0 0 100.00%
1PG 4 5924 21 4 15 8 10 7 1 6 98.73%
1NG 0 7 5946 6 7 10 3 10 9 2 99.10%
1PN 4 6 2 5967 1 1 5 6 2 6 99.45%
2PG 6 7 4 10 5951 1 3 3 9 6 99.18%
2NG 10 9 4 10 1 5957 0 5 1 3 99.28%
2PN 4 8 8 9 10 2 5937 10 6 6 98.95%
3PG 9 4 9 7 5 6 2 5951 6 1 99.18%
3NG 1 8 0 1 2 3 5 3 5976 1 99.60%
3PN 6 2 6 2 1 1 8 4 3 5967 99.45%
Total 99.29%

Output results of different fault types

Fault type Output result
NF 0 0 0 0 0 0 0 0 0
1PG 1 0 0 0 0 0 0 0 0
1NG 0 1 0 0 0 0 0 0 0
1PN 0 0 1 0 0 0 0 0 0
2PG 0 0 0 1 0 0 0 0 0
2NG 0 0 0 0 1 0 0 0 0
2PN 0 0 0 0 0 1 0 0 0
3PG 0 0 0 0 0 0 1 0 0
3NG 0 0 0 0 0 0 0 1 0
3PN 0 0 0 0 0 0 0 0 1

Various types of prediction accuracy, recall rate, F1 value

Category Accuracy Recall F1
NF 100.00% 98.32% 99.15%
1PG 98.73% 96.68% 97.70%
1NG 99.10% 95.05% 97.03%
1PN 99.45% 98.09% 98.77%
2PG 99.18% 99.37% 99.28%
2NG 99.28% 97.02% 98.14%
2PN 98.95% 96.36% 97.64%
3PG 99.18% 98.54% 98.86%
3NG 99.60% 98.37% 98.98%
3PN 99.45% 97.97% 98.70%
Total 99.29% 97.53% 98.39%
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