[1]姜艳华,王彦文.自适应神经网络在UPQC补偿量检测中的应用[J].南京理工大学学报(自然科学版),2015,39(02):225-228.
 Jiang Yanhua,Wang Yanwen.Application of adaptive neural network in compensation detection for UPQC[J].Journal of Nanjing University of Science and Technology,2015,39(02):225-228.
点击复制

自适应神经网络在UPQC补偿量检测中的应用
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
39卷
期数:
2015年02期
页码:
225-228
栏目:
出版日期:
2015-04-30

文章信息/Info

Title:
Application of adaptive neural network in compensation detection for UPQC
作者:
姜艳华12王彦文1
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
Author(s):
Jiang Yanhua 12Wang Yanwen 1
1.School of Mechanical Electronic and Information Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China; 2.School of Mechanical Engineering,Liaoning Technical University,Fuxin 123000,China
关键词:
补偿量检测 统一电能质量控制器 线性神经元
Keywords:
compensation detection unified power quality controllers linear neuron
分类号:
TM76
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。
Abstract:
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.

参考文献/References:

[1] 高大威,孙孝瑞.基于自适应线性神经元网络的三相畸变电流检测方法及实现[J].中国电机工程学报,2001,21(3):50-53.
Gao Dawei,Sun Xiaorui.A detecting approach of three-phase distortion currents based on adaptive linear neural network and its being realized[J].Proceedings of the CSEE,2001,21(3):50-53.
[2]Tey L H,So P L,Chu Y C.Improvement of power quality using adaptive shunt active filter[J].IEEE Transactions on Power Delivery,2005,20(2):1558-1568.
[3]何娜,黄丽娜,武健,等.一种新型快速自适应谐波检测算法[J].中国电机工程学报,2008,28(22):124-129.
He Na,Huang Lina,Wu Jian,et al.A novel adaptive harmonic detecting algorithm[J].Proceedings of the CSEE,2008,28(22):124-129.
[4]周海亮,万健如,李树超,等.电压畸变和不平衡状态下无锁相环UPQC补偿量检测方法[J].电力自动化设备,2012,32(5):50-56.
Zhou Hailiang,Wan Jianru,Li Shuchao,et al.Compensation detection for UPQC without PLL under voltage distortion and unbalance[J].Electric Power Automation Equipment,2012,32(5):50-56.
[5]谢少辉.基于神经网络的电能质量控制器的研究[D].北京:华北电力大学电气与电子工程学院,2009.
[6]赵小林.并联型三相四线制有源电力滤波器的研究[D].无锡:江南大学机械工程学院,2012.

备注/Memo

备注/Memo:
收稿日期:2014-03-17 修回日期:2014-10-21
基金项目:辽宁省教育厅重点实验室基金(LS2010078)
作者简介:姜艳华(1974-),女,讲师,硕士,主要研究方向:电能质量检测与控制,E-mail:jiangyanhuazly@163.com。
引文格式:姜艳华,王彦文.自适应神经网络在UPQC补偿量检测中的应用[J].南京理工大学学报,2015,39(2):225-228.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2015-04-30