一种基于BP神经网络的谐波检测方案

摘要

General harmonic detecting scheme uses the Fast Fourier Transform (FFT) to detect all the harmonics (such as from 1st harmonic to 50th harmonic). Power consumers often do not care about the specific values of all the harmonics, but only some key harmonics or several overall indicators. For the above reasons, a new harmonic detecting scheme based on the algorithm of BP neural network is presented. It does not need to calculate all the harmonics and can detect individual indicators or overall indicators which are concerned by users. To achieve the above-mentioned detection target, the computation analysis of BP, DFT and FFT algorithm is made, and the superiority of the scheme in terms of computation is proved. After the simulation of the scheme is done using a set of measured harmonic data, the results validate that the above scheme is simple and feasible, and its detecting accuracy is close to that of FFT. This work is supported by National Natural Science Foundation of China (No. 50707010).%通常的谐波检测方案是对各次谐波（如1～50次）使用快速傅里叶变换（FFT）进行检测，然而用电单位往往并不关心所有次数谐波的具体数值，而仅关心关键次数的谐波或几个总体指标。为此，设计了一套新的谐波检测方案，以 BP 神经网络作为实现算法，不需要计算各次谐波即可实现对用户所关心的个别指标或总体指标的检测，而且要实现上述检测目标，通过对BP算法、DFT算法、FFT算法进行计算量分析，证明了该方案在计算量方面的优越性。使用一组实测谐波数据对方案进行仿真验证，结果表明该方案简单可行，可达到与FFT相近的检测精度。