School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
School of Electronic Information, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2018 Jun 14;18(6):1941. doi: 10.3390/s18061941.
A power line is particularly vulnerable to wildfires in its vicinity, and various damage including line tripping can be caused by wildfires. Using remote sensing techniques, a novel model developed to assess the risk of line tripping caused by the wildfire occurrence in high-voltage power line corridors is presented. This model mainly contains the wildfire risk assessment for power line corridors and the estimation of the probability of line tripping when a wildfire occurs in power line corridors. For the wildfire risk assessment, high-resolution satellite data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and digital elevation model (DEM) data were employed to infer the natural factors. Human factors were also included to achieve good reliability. In the estimation of the probability of line tripping, vegetation characteristics, meteorological status, topographic conditions, and transmission line parameters were chosen as influencing factors. According to the above input variables and observed historical datasets, the risk levels for wildfire occurrence and line tripping were obtained with a logic regression approach. The experimental results demonstrate that the developed model can provide good results in predicting wildfire occurrence and line tripping for high-voltage power line corridors.
输电线特别容易受到附近野火的影响，各种损坏包括线路跳闸都可能由野火引起。利用遥感技术，提出了一种新的模型来评估高压输电线路走廊中因野火发生而导致线路跳闸的风险。该模型主要包括对输电线路走廊的野火风险评估，以及当野火发生在输电线路走廊时线路跳闸的概率估计。对于野火风险评估，使用高分辨率卫星数据、中分辨率成像光谱仪 (MODIS) 数据、气象数据和数字高程模型 (DEM) 数据来推断自然因素。还包括人为因素以达到良好的可靠性。在线路跳闸概率的估计中，选择植被特征、气象状况、地形条件和输电线路参数作为影响因素。根据上述输入变量和观测到的历史数据集，采用逻辑回归方法获得野火发生和线路跳闸的风险水平。实验结果表明，所开发的模型可以为高压输电线路走廊的野火发生和线路跳闸预测提供良好的结果。
