RPROP神经网络的电力系统谐波分析

将RPROP(Resilient Propagation)神经网络运用到电力系统的谐波分析中,能够提高其精度以及速度.与BP(Back Propagation)不同,RPROP算法能够调整可变参数,因此能够避免一阶偏导数幅值信息对参数调整的影响,并且能够提高谐波分析的精确度以及收敛速度.通过对RPROP神经网络与BP神经进行比较,验证了基于RPROP神经网络的电力系统谐波分析具有的高精度以及收敛速度快等特点.

Power System Harmonic Analysis Based on RPROP ANNs

For improving the speed and accuracy of harmonic analysis,a harmonic analysis method based on RPROP neural network is proposed.Hanning-windowed interpolation harmonic analysis algorithm is used to obtain initial weight and bias values of ANNs（Artificial Neural Networks）and the network takes RPROP algorithm as a training algorithm derived number.Different from the BP（Back Propagation）algorithm,the algorithm adjusts the parameters of ANNs by sign information of first-order partial derived number,which avoids the influence of amplitude information of first-order partial derived number which is useless for parameters adjustment.In the meantime,the algorithm does not have the problem of parameters selection.Therefore,the convergence speed,accuracy and real-time performance of power system harmonic analysis can be improved.Finally,BP ANNs and RPROP ANNs are used to analyze signal,and the comparison of the results verifies the conclusion.