基于GA优化BP神经网络的小电流接地故障选线方法

将GA优化BP神经网络的算法引入到小电流接地故障选线方法中。文中基于MATLAB进行仿真试验，通过小波包法、五次谐波法、基波比幅比相法及零序有功功率法等传统选线方法，将零序电流信号的各种特征量进行提取，经过故障测度函数计算得到故障测度数据，将数据分别输入到GA-BP神经网络与单一BP神经网络进行训练和测试，讨论GA-BP神经网络算法与单一BP神经网络算法选线性能的差异，输出故障选线结果并与基于各选线方法的故障测度数据进行对比。结果表明，综合多种传统选线方法的GA-BP神经网络准确率明显高于传统选线方法，且其选线速度与精度优于单一BP神经网络，能够更快速、有效地进行故障选线，满足配电网故障选线要求。

Line selection method of small current grounding fault based on GA optimized BP neural network

The algorithm of GA optimized BP neural network is introduced into the the line selection method of small current grounding fault. The article conducts simulation experiment based on Matlab: extracts the various characteristic quantities of zero-sequence current signal by traditional line selection methods, such as wavelet packet method, fifth harmonic method, fundamental wave ratio, amplitude ratio and phase ratio method,zero-sequence active power method and the like, calculates the fault measure data using fault measure function, then inputs the data into GA-BP neutral network and single BP neutral network for training and testing, discusses the difference in line selection performance between GA-BP neural network algorithm and single BP neural network algorithm, and outputs the fault line selection results and compares with the fault measure data based on each line selection method. In conclusion, the GA-BP neutral network combining multiple traditional line selection methods has much higher accuracy rate than transitional counterparts, and its selection speed and precision is superior over single BP neutral network, and can carry out fault line selection in a quick and effective way, satisfying the requirements of fault line selection in the power distribution network.