Research on Fire Prediction Method of High-Voltage Power Cable Tunnel Based on Abnormal Characteristic Quantity Monitoring

Chenying Li,Jie Chen,Xiao Tan,Libin Hu,Jingxing Cao,Jianjun Liu,Fengbo Tao,Ziheng Pu

doi:10.3389/fenrg.2022.836588

Abstract

The proportion of cable lines in the urban distribution network is increasing. The fire hazard of important cable channels is prominent, which has a serious effect on the safety and stable operation of the power system. In recent years, intelligent mobile inspection and fire extinguishing devices have been applied in tunnels. The determination of firepower and location is conducive to the rapid and effective fire suppression of intelligent devices. Therefore, this study proposes a fire early warning method of a high-voltage power cable tunnel based on abnormal characteristic quantity monitoring. Based on the modified model of complex pyrolysis and combustion chemical reaction, the fire development of different fire source powers and fire locations is simulated. The temperature distribution and characteristic gas concentration under different simulation conditions are analyzed. The results show that the monitoring data of temperature, flue gas concentration, and CO and CO2 concentration need comprehensive analysis to effectively reflect different fire conditions. The characteristic data set is selected and processed to form a total sample. The fire prediction model is trained and tested. The accuracy of the proposed prediction model is 92%.

Highlights

With the growth of power demand, more and more power cables are laid intensively in cable tunnels
The key to the problem lies in how to make full use of the characteristic information contained in the monitoring data and use mathematical models and intelligent algorithms to analyze the relationship between various parameters (Shen et al, 2020; Shen et al, 2021; Shen and Raksincharoensak, 2021), through the information obtained to monitor and locate the fire in the cable tunnel, confirm the fire source information, and provide relevant information for the emergency dispatch of electric power (Yang et al, 2021a), to improve the level of fire prevention and control in cable tunnels
According to the ratio of the outer sheath and insulating layer, the molecular formula of gas-phase fuel burned by the power cable is C2H3.6Cl0.4, and the gas-phase reaction equation is shown in formula (4): FIGURE 1 | The comparison between simulation results and test data of total combustion power

Summary

INTRODUCTION

With the growth of power demand, more and more power cables are laid intensively in cable tunnels. The key to the problem lies in how to make full use of the characteristic information contained in the monitoring data and use mathematical models and intelligent algorithms to analyze the relationship between various parameters (Shen et al, 2020; Shen et al, 2021; Shen and Raksincharoensak, 2021), through the information obtained to monitor and locate the fire in the cable tunnel, confirm the fire source information, and provide relevant information for the emergency dispatch of electric power (Yang et al, 2021a), to improve the level of fire prevention and control in cable tunnels Emerging technologies such as artificial intelligence algorithms, especially deep learning, computer vision, and other related technologies, have developed rapidly (Yang et al, 2021b; Yang et al, 2021c; Yang et al, 2022). Some of them are used as training samples to train the early warning model and use the test samples to test and verify the accuracy of the early warning model

Simulation Method of Power Cable Combustion

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT