基于改进级联神经网络自适应电网谐波检测

为克服电网谐波检测快速性与稳定性矛盾，基于神经网络自适应原理提出了一种级联神经网络自适应电网谐波检测的改进系统。改进级联系统初级运用大步长常规LMS(Least Mean Square)自适应神经网络单元提高检测跟随性能，次级通过嵌入均值滤波环节平滑权值波动的策略构造新的自适应神经网络单元，保证次级神经网络单元具有良好的电网谐波检测稳态精度。运用传递函数Z域变换分析嵌入均值滤波环节的电网谐波检测自适应神经网络单元的稳定性能，运算推导新的级联次级神经网络自适应单元的步长约束条件，保证改进系统既能够有效地提高电网谐波检测的跟随性能同时又可以提高检测的稳态精度。仿真实验表明改进的级联神经网络自适应系统能有效提高电网谐波检测动态性与精确性。

Adaptive detection of harmonic current in power grid based on improved cascade neural network

A novel adaptive system based on cascade detecting harmonic current in power system is proposed to solve the contradiction between rapidity and stability. In cascade neural network, a fast dynamic response of harmonic detection can be provided by normal least mean square (LMS) with large step-size, meanwhile to filter fluctuation of weight, a higher precision of adaptive detection in steady-state is introduced by embedding an average filter into LMS. By using Z-transform, this paper analyses the stabilization and derives constraint conditions of step-size of the novel neural network unit based on LMS with embedded average filter. The stability of the new system is guaranteed by the limited range of specified step-size to improve dynamic performances and reduce steady-state errors of adaptive detection. Simulation results show that the improved cascade neural network has faster dynamic response and higher accuracy in adaptive harmonic detection.