基于贝叶斯线性回归的鸟害故障分析

Analysis of bird caused transmission line fault based on Bayesian linear regression

近年来,害鸟引起广东电网输电线路故障所占比例逐渐增高,成为电网安全的主要隐患之一.如何降低鸟害故障,已经成为输电线路运行维护所面临的一个新的课题.输电线路分布区域广,盲目的人工驱赶鸟类难以有效防止鸟害,因此通过对鸟害故障进行分析是防止鸟害的有力支持.通过收集广东电网2015—2019年5年来的鸟害运维数据,根据鸟害故障的地理环境特征、杆塔结构特征与季节特征,建立鸟害故障分析模型.首先,分别分析地理特征、杆塔结构特征以及不同季节对鸟害故障的影响,然后训练Mask R-CNN神经网络提取杆塔周围的地理环境特征,最后建立基于贝叶斯线性回归的鸟害故障分析模型,并使用相关系数R;评估模型的精度.实验结果表明,本文所构建的鸟害故障分析模型具有较高的准确性和可靠性.

In recent years, the transmission line faults caused by birds in Guangdong power grid have been increasing gradually, which have become one of the main hidden dangers for power grid security.How to reduce the bird damage has become a new topic of transmission line operation and maintenance.For the widely distributed transmission lines, it is difficult to effectively prevent bird damage by driving birds approach.Here, based on the operation and maintenance data related with bird damage on Guangdong power grid from 2015 to 2019,we analyzed the geographical characteristics, pole tower structures and seasons, then established a model to analyze the transmission line faults caused by bird damage.First, the influence of geographical characteristics, pole tower structure and season on bird caused fault is analyzed.Then, a Mask R-CNN neural network is trained to extract the geographical characteristics around the pole tower.Finally, a bird damage fault model based on Bayesian linear regression is established, and the accuracy of the model is evaluated by the correlation coefficient of R;.The experimental results show that the model has high accuracy and reliability.