一种低压配电系统短路电流峰值预测的新模型

A new model for predicting peak value of short-circuit current in a low-voltage distribution system

针对少样本、非指数特征、随机性强的原始数据序列和低压配电系统全局选择性保护对短路电流峰值预测的实时快速性特殊要求,根据灰色预测理论建立适合短路电流这一类数据特征的灰色预测新模型,通过引入递推结构、选择合适的外推因子等方式极大地提高预测的时效性和精确度.将该模型应用于实际低压配电系统,经过几类模型的对比测试,说明所提出的新模型能够实现短路电流峰值这类数据的快速、高精度的预测.

The original data sequence may have less sample,non-index,strong random features.And the real time and the rapidity are the special requirements for the peak value prediction of the short-circuit current according to the global selective protection in a low-voltage distribution system.So a new grey predictive model is built based on the grey prediction theory suitable for these data,such as the short-circuit current data.The recursive structure is built and the appropriate extrapolation factor is selected to improve the timeliness and the precision greatly.Finally,this new model is applied to a low-voltage distribution system to achieve the online rapid prediction for the peak value.The comparison tests are done by some other kinds of grey models.The results show that the rapid high-precision prediction can be obtained by using the proposed model for these data,like the peak value of the short-circuit current.