摘要
针对灰色预测模型在电力系统短路电流峰值预测中因数据迭代不合理而不能充分利用新的有效信息的问题,提出了一种适合电力系统的灰色BP神经网络动态预测模型.该模型通过引入动态的数据迭代模型,以最小相对误差为目标参数对传统灰色模型进行改进.通过Matlab/Simulink搭建电力系统短路故障模型进行仿真分析,获得不同初相角下电力系统短路时的电流数据;将短路电流、故障初相角、灰色模型预测结果和其相对残差作为BP神经网络的输入对改进的灰色模型进行训练,得到最终的短路电流峰值预测模型.验证实验表明,该模型能够快速、准确地预测短路电流峰值,适用于原始样本点少、非线性特征显著和随机性强的复杂系统.
In view of the unreasonable data iteration and the problem that the new effective information can not be fully utilized of the grey prediction model in the short circuit curent peak prediction of power system, a greyBP neural network dynamic prediction model was proposed to adapt for power system. By introducing the dynamic data iteration model, the traditional gray model was improved with the minimum relative en＇or as thetarget parameter. The short circuit fault model of power system was built by Matlab/Simulink for simulation analysis ,and the current data of short circuit of power system under different initial phase angles were ob-tained. The improved grey model was trained by the short circuit current, the fault initial angle, the prediction result of the grey model and its relative residuals as the input of training BP neural network to obtain the finalprediction model of the short circuit current peak. Verification experiments showed that the model could achieve fast and accurate prediction of short circuit current peaks, and was suitable for complex systems withfew original sample points, significant nonlinear features and strong randomness.
作者
陈建明
张盼盼
CHEN Jianming;ZHANG Panpan(College of Electric Power,North China University of Water Resources and Electric Power,Zhengzhou 450045,Chin)
出处
《轻工学报》
CAS
2018年第4期79-85,共7页
Journal of Light Industry
关键词
电力系统
短路电流峰值预测
灰色模型
BP神经网络
全局协调性保护
power system
short circuit current peak prediction
grey model
BP neural network
global coordination protection
