基于FFT和神经网络的非整数次谐波分析改进算法(英文)  

Improved Algorithm for Non-integer Harmonics Analysis Based on FFT Algorithm and Neural Network

作　　者：王公宝[1] 向东阳[1] 马伟明[1] 

机构地区：[1]海军工程大学,湖北省武汉市430033

出　　处：《中国电机工程学报》2008年第4期102-108,共7页Proceedings of the CSEE

基　　金：高等学校优秀青年教师教学科研奖励计划基金项目(教育部人事司2001-182)

摘　　要：运用神经网络模型进行整数次谐波检测可达到较高的检测精度,但该种线性神经元模型不适合非整数次谐波的检测。为精确检测非整数次谐波,该文提出一种改进的线性人工神经元模型,并将加汉宁窗的FFT算法和改进的线性人工神经元模型结合起来,提出一种改进的非整数次谐波分析算法。首先,对采样信号用加汉宁窗的FFT算法进行预处理,得到谐波个数和精度不高的谐波次数;其次,根据谐波个数设定神经元的个数,根据预处理后得到的谐波次数设定神经网络谐波次数迭代的初始值;为了提高迭代速度,提出了谐波次数迭代步长自适应调整的算法。最后对改进后的人工神经网络进行训练,实现了非整数次谐波的精确检测。仿真实例表明,该方法能将频率相近的非整数次谐波分离,可有效提高谐波参数的检测精度和速度。By using an artificial neural network （ANN） model, high measurement accuracy of integer harmonics can be obtained. Combining the windowed fast Fourier transform （FFT） algorithm with the improved ANN model, the paper provides an improved algorithm for analysis of non-integer harmonics in electric power systems. Firstly, the Hanning- windowed FFT algorithm processes the sampled signal. By this time, the number of harmonics and the orders of harmonics are obtained. Secondly, choose the number of neural nodes according to the number of harmonics. Thirdly, choose the initial values of orders of harmonics according to the result obtained from the Hanning-windowed FFT algorithm. Moreover, an adaptive algorithm for the adjusting step of the order of harmonic is presented. Finally, by using the improved linear ANN model obtained in the paper, non-integer harmonics can be detected precisely. Through such processing, the time of iterations is shortened and the convergence rate of neural network is raised thereby. The simulation results show that close non-integer harmonics can be separated from a signal with higher accuracy and better real-time by using the improved algorithm presented.

关 键 词：电力系统 快速傅里叶变换 人工神经网络 汉宁窗 谐波分析 

分 类 号：TM935[电气工程—电力电子与电力传动]