气象过程信息挖掘与输电线路覆冰预测

Meteorological Processes Information Mining and Transmission Lines Icing Forecast

针对现有覆冰预测回归模型以采样点气象参量预报覆冰值的局限性,提出了一种改进的基于气象过程信息挖掘的覆冰预测方法。按线路覆冰增量将气象参量样本分为覆冰增长、维持、消融3个模糊模式类别,定义了以气象参量样本与模式类别中心的马氏距离为变量的隶属度函数,并在计算马氏距离时采用灰色斜率关联度确定各气象参量的权重。基于此,将隶属度与采样点气象参量结合,形成包含覆冰气象过程信息的高维历史数据样本,采用支持向量机进行覆冰回归模型的训练与预测。算例比较了现有的神经网络、支持向量机预测方法与提出的改进预测方法,结果表明,前两者预测的相对误差均值分别为24.50%和22.66%,而改进的预测方法相对误差均值为6.62%。考虑气象过程信息挖掘的覆冰预测模型具有更高的预测精度。

In view of the imperfection of existing predictive regression models for icing forecast where only meteorological parameters at one moment are used to foretell the predicted icing value at the same moment,a revised icing forecast method based on meteorological process information mining is proposed.The samples of meteorological parameters are divided into three fuzzy pattern categories,i.e.,icing growing,sustaining and melting.Then the membership function with the variable symbolized by Mahalanobis distance from meteorological parameters sample to the center of categories is defined.And the method of gray slope-correlation is used to determine the weights of meteorological parameters to evaluate the Mahalanobis distance.The degrees of membership and the samples of meteorological parameters are combined to form the high dimensional historical samples containing icing meteorological processes information,then support vector machine method is used to train the icing forecast regression function and predict.Numerical tests are conducted and the results indicate that the mean relative error in neural network method gets 24.50％ and 22.66％ in the current support vector machine method,but 6.62％ in the revised cases.The icing forecasting model based on the meteorological processes information mining is endowed with higher predicting accuracy.