基于RPROP神经网络的电力系统谐波分析

为了提高谐波分析的速度和精度,将RPROP（Resilient Propagation）神经网络应用于电力系统谐波分析。该网络利用加汉宁窗插值谐波分析算法获得其权值和阈值的初值,并在此基础上采用RPROP算法训练。与BP（BackPropagation）算法不同,该算法根据一阶偏导数的符号信息调整可变参数,避免了受对参数调整意义不大的一阶偏导数幅值信息的影响,且不存在参数选择问题,提高了谐波分析的收敛速度、精确度和实时性。通过变学习速率且加动量项的BP神经网络与RPROP神经网络的比较验证了分析结论的正确性。...

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Bibliographic Details
Published in电力系统保护与控制 Vol. 39; no. 15; pp. 13 - 16
Main Author 徐志钮 律方成
Format Journal Article
LanguageChinese
Published 华北电力大学生物质发电成套设备国家工程实验室,河北,保定,071003 2011
Subjects
RPROP算法
汉宁窗
电力系统
神经网络
谐波分析
RPROP算法
谐波分析
神经网络
汉宁窗
电力系统
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Summary:为了提高谐波分析的速度和精度,将RPROP（Resilient Propagation）神经网络应用于电力系统谐波分析。该网络利用加汉宁窗插值谐波分析算法获得其权值和阈值的初值,并在此基础上采用RPROP算法训练。与BP（BackPropagation）算法不同,该算法根据一阶偏导数的符号信息调整可变参数,避免了受对参数调整意义不大的一阶偏导数幅值信息的影响,且不存在参数选择问题,提高了谐波分析的收敛速度、精确度和实时性。通过变学习速率且加动量项的BP神经网络与RPROP神经网络的比较验证了分析结论的正确性。
Bibliography:XU Zhi-niu,Lü Fang-cheng （National Engineering Laboratory for Biomass Power Generation Equipment,North China Electric Power University,Baoding 071003,China）
power system; harmonic analysis; RPROP algorithm; neural network; Hanning window
41-1401/TM
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 （BackPropagation）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 performanc
ISSN:1674-3415
DOI:10.3969/j.issn.1674-3415.2011.15.003