计及空间相关性的架空线路载流量预测方法

Prediction Method for Carrying Capacity of Overhead Transmission Line Measuring Spatial Correlation

将风速的空间相关性引入架空线路载流量预测中,首先以600 m×600 m划分气象–地形网格,使用Pearson相关系数(Pearson correlation coefficient,PCC)方法分析风速与地形的相关性,结果表明风速与地形的坡度、坡位和地表粗糙度相关性较强。然后将地形和风速关联数据带入到改逆向传播(back propagation,BP)神经网络,得到可根据地形预测实时风速的神经网络。该方法的预测误差约为35%,如果能获得更精确的风速数据,可以提高该方法的精度。最后将风速预测方法与最大太阳辐射预测方法相结合,计算某具体线路的载流量,验证了该方法的实用性。

Spatial correlation of wind speed was introduced into predicting carrying capacity of the overhead transmission line which firstly divided meteorological-terrain network by 600 m X 600 m and analyzed correlation between wind speed and ter-rain by means of Pearson correlation coefficient （PPC） method. Corresponding results indicate that correlation with gradient , status and surface roughness of terrain. Then associated data of terrain and wind speed wasbrought into the improved back propagation （BP） neural network and a neural network for predicting real-time wind speedaccording to the terrain was obtained. The prediction error of this method is about 35 % 〇 and it is able to improve precision of this method if getting more precise wind speed data. Finally, this prediction method for wind speed was combined with the prediction method for the maximum solar radiation to calculate carrying capacity of one specific line , whicbility of this method.