短路电流峰值对低压系统选择性保护及其断路器可靠分断十分重要，迄今尚缺乏深入研究。利用短路故障早期检测技术，在仿真分析短路故障早期参数的基础上，采用灰度关联度，得出对短路电流峰值的主要影响因素，并采用极端学习机(ELM)实现短路电流峰值的预测。仿真结果表明，灰色关联度可有效辨识短路电流主要因素，降低了短路电流预测特征变量维数。基于短路故障早期检测及极端学习机的短路电流预测方法，具有鲁棒性强且精度高的特点，为低压选择性保护技术的实现奠定基础。%The peak of short-circuit current is of great importance for selective protection of low-voltage distribution system and reliable breaking of circuit breaks. However, it is lack of intensive study now. Based on the early fault detection technology and the parameters analyzed by simulation, this paper concludes the main factors which influence the peak value by using grey correlation degree. Furthermore, the extreme learning machine (ELM) is used to forecast the peak value of fault current. Simulation results show that the grey correlation degree can identify the main factors of the short-circuit current effectively. And it can also reduce the dimensions of characteristic variable of short-circuit current. Finally, the short-circuit current prediction method based on the early fault detection and extreme learning machine shows strong robustness and high precision, which can lay the foundation for the realization of low-voltage selective protection technology.
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