电网技术2011，Vol.35Issue(8)：124-128,5.
基于前馈神经网络的电网基波高精度检测
High Precision Detection of Fundamental of Power Grid Based on Back Propagation Neural Network
摘要
Abstract
Fundamental of power grid is an important index for electric energy metering and power quality evaluation. A high-precision detection approach, which is based on back propagation neural network （BPNN）, for the frequency and amplitude of power grid fundamental is proposed. It is derived mathematically that the relationship of the time difference, which is between zero-crossing point of sinusoidal signal and the intersection point of time axis and the line connecting two symmetric points on signal curve at both sides of the zero-crossing point, to signal frequency is not strictly linear but monotonous, and the relationship is independent of the amplitude of the signal. Accordingly, the mapping relation between the time difference and fundamental frequency is built by BPNN. Results of simulation by Matlab show that using the proposed algorithm the detection accuracy of fundamental frequency is 10-4 and the detection accuracy of fundamental amplitude is as high as 10-5, and these detection results are sharply higher than those by interpolation algorithms based on fast Fourier transform （FFT） and Hamming window; random noise and harmonics slightly influence the measuring accuracy by BPNN, so the proposed algorithm possesses strong anti-interference capability.关键词
电网基波/前馈神经网络/基波频率/基波幅值Key words
fundamental/back propagation neural network （BPNN）/fundamental frequency/fundamental amplitude分类
信息技术与安全科学引用本文复制引用
王勇,付志红,张淮清,王好娜,侯兴哲..基于前馈神经网络的电网基波高精度检测[J].电网技术,2011,35(8):124-128,5.基金项目
基金项目：国家自然科学基金项目（40874094）. （）
