基于改进型小波神经网络的谐波检测方法

随着大功率器件使用,造成电网中有大量谐波,威胁设备的安全。提出运用小波神经网络(Wave Neural Network,WNN)算法来检测谐波。首先,针对神经网络初始值设置不当导致的网络收敛慢甚至不收敛的问题,提出了网络初始参数自相关修正的优化方法,提高了网络的性能。其次,运用附加动量项的训练算法平滑了权值学习路径,有效避免了网络训练陷入局部最小,提高了谐波检测精度。最后,经过与其它检测方法的仿真对比,证明了所述方法具有收敛速度快,检测精度高的优点。

Harmonic detection based on Improved Wavelet Neural Network

With the use of high power devices, resulting in a large number of harmonics, threat the safety of equipment. This paper proposes the use of wavelet neural network (wave neural network WNN) algorithm to detect harmonics. Firstly, the initial value of neural network convergence set due to improper slow or even non convergence problems, put forward a method of optimal initial parameters correlation correction, improve the network performance. Secondly, using smoothing training algorithm with additional momentum item weights learning path, avoid network training into local minimum, to improve the precision of harmonic detection. Finally, through simulation and comparison with other detection methods, proved that the method presented in this paper has advantages of fast convergence speed, detection high precision.