基于数据驱动算法和LS-SVM的输电线路覆冰预测

Transmission Line Icing Prediction Based on Data Driven Algorithm and LS-SVM

输电线路的严重覆冰给电网的安全生产带来了极大的危害。针对线路覆冰与气象条件的数学描述模型不能很好地反应冰厚与微气象条件的关系和基于全局数据的智能覆冰预测算法复杂、准确度低,无法实现在线预测的难题,文中采用数据驱动的思想,以矢量的方式看待覆冰样本数据,提出一种基于数据驱动算法和最小二乘支持向量机(LS-SVM)覆冰预测模型。该方法在k均值邻近算法的基础上对覆冰历史数据进行优化选择,充分利用LS-SVM需求样本数量少、训练速度快、泛化能力强等特点对输电线路覆冰模型进行快速建模。算例表明了所提算法的有效性和正确性。

Serious icing on the transmission line leads to great damage to the safety production of the grid.Owing to the inability of mathematical models describing line icing and meteorological condition to present the relation between ice thickness and micro meteorological condition plus the complexity and low accuracy of the intelligent icing prediction algorithm based on the global data,on-line prediction cannot be realized.In view of the above problems,the concept of data driven is adopted to propose an icing prediction model based on the data driven algorithm and least squares-support vector machine(LS-SVM).The icing sample data are dealt with as vectors.This method makes optimization selection of icing historical data based on the k-vector nearest neighbors (k-VNN) method and realizes fast modeling with LS-SVM with the advantages of small sample,fast training and strong generalization ability.The case study shows the effectiveness and correctness of the proposed method.