基于BP神经网络的输电线路山火风险评估模型

近年来，输电线路因山火引起的跳闸停电事故越来越多，严重影响了电网的安全稳定，山火风险防控俨然已成为电网防灾减灾的重要研究课题。考虑到输电线路山火风险的影响因素多而复杂，提出了一种基于BP神经网络的山火风险评估模型。通过研究分析220 kV及以上输电线路山火灾害高发的实际情况，确定山火主要影响因子作为模型的输入，将山火风险等级作为模型的输出，利用Matlab建立基于BP神经网络的山火风险评估模型。实验结果表明该模型能有效地预测山火风险，对及时发布预警消息具有重要意义。

Modeling forest fire risk assessment based on BP neural network of transmission line

The power outage accidents of transmission line happened more and more frequently because of forest fire disasters, which seriously affected the safety and stability of the grid. Forest fire risk the transmission line is becoming an important research topic for disaster prevention and mitigation of grid. Considering the complexity and variability of fire risk factors, this paper proposes a forest fire risk assessment model based on BP neural network of transmission line. Combining with the actual situation of high incidence of forest fire disasters in some places of 220 kV transmission lines and above, several factors and the actual fire assessment grade are chosen to serve as the input of model and the output of model respectively. Thus, the risk assessment model is established. The experiment results show that the model predicts fire risk of transmission line effectively, which is of great significance to give a warning message timely for grid.