基于NNAC算法的电能质量暂态扰动检测  

Power quality transient disturbance detection based on neural network adaptive control algorithms

作　　者：李加升[1] 王应德[2] 林琳[1] 

机构地区：[1]湖南城市学院通信与电子工程学院,湖南益阳413000 [2]长沙学院电子与通信工程系,长沙410003

出　　处：《计算机工程与应用》2013年第17期125-128,共4页Computer Engineering and Applications

基　　金：湖南省科技计划项目(No.2010GK3179);益阳市科技计划项目(No.2011JZ46)

摘　　要：针对当前电能质量检测分析的难点和重点问题,在分析了目前使用最多的方法小波变换优缺点的基础上,提出了基于神经网络自适应控制(NNAC)的电能质量暂态扰动检测算法。给出了电能质量暂态扰动检测的自适应控制结构,采用Hebb学习规则进行权值学习,并对电压暂降、电压瞬升、电压中断和暂态振荡等暂态扰动进行了仿真测试,结果表明所提算法可以很好地检测电网中的暂态扰动信号的类型,确定扰动发生的起始时刻和持续时间,且分析计算简单,速度快,计算所得数据量少,在电能质量扰动检测中更加具有实时性。Aiming at current difficulties and key issues of power quality detection, after analyzing the advantages and disadvan- tages of the method most used based on wavelet transform, an algorithm of power quality transient disturbances detection based on the adaptive control using neural networks is proposed. The adaptive control structure of power quality transient disturbances detection is provided. Hebb rule is applied to learn the weight. Simulation test is conducted on transient disturbance of voltage sag, voltage transient rise, voltage interruption and transient oscillations. The results show that this algorithm behaves well on detecting the types of transient disturbances signal, confirming the starting time and duration of disturbance. Besides, the analy-sis and calculation of this algorithm are simple and fast, the datum is small, all of which make the algorithm practical on power quality transient disturbances detection.

关 键 词：神经网络 自适应控制 HEBB学习规则 暂态扰动 

分 类 号：TM714[电气工程—电力系统及自动化]