自适应神经网络在UPQC补偿量检测中的应用  

Application of adaptive neural network in compensation detection for UPQC

作　　者：姜艳华[1,2] 王彦文[1] 

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083 [2]辽宁工程技术大学机械工程学院,辽宁阜新123000

出　　处：《南京理工大学学报》2015年第2期225-228,235,共5页Journal of Nanjing University of Science and Technology

基　　金：辽宁省教育厅重点实验室基金(LS2010078)

摘　　要：为改善电网的电能质量,提出一种新的统一电能质量控制器(UPQC)补偿量检测方法。利用线性神经元在线自适应调整学习率获得基波电压分量,再根据对称分量法提取基波正序电压分量,获得电压补偿量,利用已得结果和能量平衡原理计算得到电流补偿量。结果表明,基于该方法设计的UPQC能够有效地对电能质量进行补偿,其动态响应速度快,结构简单,易于硬件实现。仿真结果验证了该文方法的正确性。A novel compensation detecting method for the unified power quality controller（UPQC）is proposed for improving the power quality of the power grid. The fundamental voltage component is obtained by the online adaptive adjustable learning rate of the linear neuron, positive sequence voltage components of fundamental are extracted based on the symmetrical component method, and the voltage compensation is acquired. By taking advantage of available results, the current compensation is calculated according to the energy balance principle. The results show that the designed UPQC can effectively compensate for the power compensation with fast dynamic response,it has the simple structure and can be easily implemented in hardware. The correctness of the detecting method is proved by simulation results.

关 键 词：补偿量检测 统一电能质量控制器 线性神经元 

分 类 号：TM76[电气工程—电力系统及自动化]