基于BP神经网络的悬垂绝缘子串风偏角预测模型

利用有限元方法模拟不同导线型号、导线初始应力、档距、高差等结构参数的输电线路在随机风作用下的动力响应,得到悬垂绝缘子串的风偏角.进而基于有限元模拟结果和BP神经网络构建风偏角的预测模型,将导线型号、档距、高差、导线初始应力、基本风速、保证系数作为模型的输入,悬垂绝缘子串的风偏角作为输出,通过机器学习,并采用评价指标评估其准确性,对模型进行优化.该模型可以方便快捷地预测悬垂绝缘子串的风偏角,为线路塔头绝缘设计提供依据.

BP neural network model for dynamic swing angle of suspended insulator string

The dynamic swing angles of suspended insulator strings of transmission lines with different parameters including conductor type, initial stress in conductor, span length and height difference, under stochastic wind field are numerically simulated by means of the finite element method. Based on the finite element simulation results and the BP neural network, a prediction model for the swing angle is constructed. In the model, the conductor type, span length, height difference, initial stress, wind speed and guarantee factor are taken as the input parameters, and the swing angle as the output parameter. The model is optimized by machine learning and accuracy evaluation with specific evaluation indicators. Swing angles of suspended insulator string in stochastic wind field can be predicted conveniently and fast with this model, which provides a basis for the insulation design of tower head in transmission lines.