Research on Self-Recovery Ignition Protection Circuit for High-Voltage Power Supply System Based on Improved Gray Wolf Algorithm

In order to solve the problems of traditional high-voltage power supply ignition protection circuits, such as non-essential start–stop power supply, a slow response speed, the system needing to be restarted manually, and so on, a high-voltage power supply system self-recovery ignition protection circuit was designed using an IGWO (improved grey wolf optimization) and PID control strategy designed to speed up the response speed, and improve the reliability and stability of the system. In high-voltage power supply operation, the firing discharge phenomenon occurs. Current transformers fire signal into a current signal through the firing voltage value and Zener diode voltage comparison to set the safety threshold; when the threshold is exceeded, the fire protection mechanism is activated, reducing the power supply voltage output to protect the high-voltage power supply system. When the ignition signal disappears, based on the IGWO-PID control of the ignition self-recovery circuit according to the feedback voltage, the DC supply voltage of the high-voltage power supply is adjusted, inhibiting the ignition discharge and, according to the ignition signal, “segmented” to restore the output of the initial voltage. MATLAB/Simulink was used to establish a system simulation model and physical platform test. The results show that the protection effect of the designed scheme is an improvement, in line with the needs of practical work.

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Publication Details

Journal
Energies
Published
2024-12-16
DOI
https://doi.org/10.3390/en17246332
Primary Topic
High-Voltage Power Transmission Systems
Type
article
Field-Weighted Citation Impact
0.00

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Research on Self-Recovery Ignition Protection Circuit for High-Voltage Power Supply System Based on Improved Gray Wolf Algorithm

Ai‐Min Zhu, Haifeng Wei, Yi Zhang, Jingyi Zhu
Energies
High-Voltage Power Transmission Systems
article

Research on Self-Recovery Ignition Protection Circuit for High-Voltage Power Supply System Based on Improved Gray Wolf Algorithm

Ai‐Min Zhu, Haifeng Wei, Yi Zhang, Jingyi Zhu
article en

Abstract

In order to solve the problems of traditional high-voltage power supply ignition protection circuits, such as non-essential start–stop power supply, a slow response speed, the system needing to be restarted manually, and so on, a high-voltage power supply system self-recovery ignition protection circuit was designed using an IGWO (improved grey wolf optimization) and PID control strategy designed to speed up the response speed, and improve the reliability and stability of the system. In high-voltage power supply operation, the firing discharge phenomenon occurs. Current transformers fire signal into a current signal through the firing voltage value and Zener diode voltage comparison to set the safety threshold; when the threshold is exceeded, the fire protection mechanism is activated, reducing the power supply voltage output to protect the high-voltage power supply system. When the ignition signal disappears, based on the IGWO-PID control of the ignition self-recovery circuit according to the feedback voltage, the DC supply voltage of the high-voltage power supply is adjusted, inhibiting the ignition discharge and, according to the ignition signal, “segmented” to restore the output of the initial voltage. MATLAB/Simulink was used to establish a system simulation model and physical platform test. The results show that the protection effect of the designed scheme is an improvement, in line with the needs of practical work.

EnergiesVol. 17(24)
Jiangsu University of Science and Technology (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province
Affordable and clean energy
Openalex Percentile: Top 79%
High-Voltage Power Transmission Systems
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Research on Self-Recovery Ignition Protection Circuit for High-Voltage Power Supply System Based on Improved Gray Wolf Algorithm — Ai‐Min Zhu, Haifeng Wei, et al. · Energies (2024) | TGRS Research Map | TGRS