基于自适应神经网络的谐波分析模型与算法

Harmonic Analysis Model and Algorithm Based on Adaptive Neural Network

提出一种周期信号的谐波基函数神经网络模型及基于该模型的谐波分析算法。该算法将基波频率和谐波幅值相位共同作为权值参与学习调整,通过自适应测量原理估计各次谐波参数,算法的收敛性定理为学习率的选择提供了理论依据。对信号存在频率偏差和含有白噪声两种情况分别进行了仿真,结果表明该算法精度高、收敛速度快,适合于非同步采样和短数据下的电力系统谐波分析。

A neural network model named harmonic basis function （HBF） of multi-frequency periodic signals is proposed, and a harmonic analysis algorithm based on the HBF model is presented. In the supposed algorithm, the fundamental frequency and the harmonic amplitude-phase parameters are introduced as weights needed to be adjusted. The harmonic parameters are estimated through the adaptive measurement theorem. The convergence theorem of the algorithm provided theoretical guides for selecting of the learning rates. Simulations are conducted on the signals with frequency deviation as well as with white noises. The results show that the algorithm achieves high accuracy and rapid speed in convergence and is a good candidate for measuring the harmonics with asynchronous sampling and short data in power systems.