配电系统短路电流峰值灰色预测模型优化

An Optimized Model on the Grey Prediction of the Peak Value of the Short-Circuit Current in a Distribution System

低压配电系统全局选择性保护对短路电流峰值预测提出了实时快速性特殊要求,以灰色预测理论为依据建立适合短路电流数据特征的传统预测模式的灰色预测模型存在理论上的缺陷,影响预测精确度.为此,在对传统灰色预测不足进行分析的基础上,通过调整初始迭代点,引入递推结构,选择合适的修正因子等方法对模型结构进行拓展,以相对误差均值最小为原则的目标函数动态选择参数,进一步优化灰色模型,将优化后的模型应用于低压配电系统,可实现短路电流峰值的快速、高精度预测.

Real time and speed are vital for the peak value prediction of the short circuit current according to the global selective protection in a low-voltage distribution system. A grey prediction model based on the traditional mode takes into account of the features of the short circuit current data, but is not accurate enough for its defects in design. Some measures are therefore used to improve the prediction accuracy against the defect of the traditional gray forecasting, including adjusting the initial point, setting up the recursive structure, expanding the structure with the appropriate correction factor, using the objective function with the principle of minimum mean relative error. This optimized model is applied to a low-voltage distribution system and the rapid high-precision prediction for the peak value is achieved according to the experimental test results.