双馈风机神经电力系统稳定器设计与仿真研究

doi:10.16182/j.issn1004731x.joss.201701022

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (1): 162-169.doi: 10.16182/j.issn1004731x.joss.201701022

双馈风机神经电力系统稳定器设计与仿真研究

牛玉广¹, 杨巍^2,3, 李晓明², 王世林², 林忠伟¹

1.新能源电力系统国家重点实验室(华北电力大学),北京 102206;
2.华北电力大学控制与计算机工程学院,北京 102206;
3.中国电力工程顾问集团华北电力设计院工程有限公司,北京 100120

收稿日期:2015-04-27 修回日期:2015-09-17 出版日期:2017-01-08 发布日期:2020-06-01
作者简介:牛玉广(1964-),男,河南,博士,教授,博导,研究方向为新能源电力系统建模与控制。
基金资助:
国家自然科学基金(51606033,61203043),国家重点基础研究发展计划(2012CB215203),中央高校基本科研业务费专项资助项目

Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG

Niu Yuguang¹, Yang Wei^2,3, Li Xiaoming², Wang Shilin², Lin Zhongwei¹

1. State Key Laboratory for Alternate Electric Power System with Renewable Energy Source, North China Electric Power University,Beijing 102206, China;
2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
3. North China Power Engineering CO. LTD., China Power Consulting Group, Beijing 100120, China

Received:2015-04-27 Revised:2015-09-17 Online:2017-01-08 Published:2020-06-01

摘要/Abstract

摘要： 为提高并网双馈风机的暂态稳定性。设计了一种基于磁链幅值相角控制(Flux Magnitude Angle Control,FMAC)的神经自适应电力系统稳定器(Neural Adaptive Power System Stabilizer,NPSS)通过在线训练Elman神经网络以实现自适应控制,利用双馈风机雅各比矩阵的符号代替雅各比矩阵运算以减少计算时间、提高运算速率。主导特征值分析和动态仿真证明神经电力系统稳定器在改善系统阻尼方面的有效性。与同步发电机(Synchronous Generation,SG)安装自动电压调节器(Automatic Voltage Regulator,AVR)和电力系统稳定器的对比仿真表明：双馈风机安装神经电力系统稳定器具有更好的阻尼特性、电压调节效果和暂态稳定性。

关键词: 双馈风机, 电力系统稳定器, 人工神经网络, 系统阻尼

Abstract: A Flux Magnitude Angle Control (FMAC) strategy based Neural Adaptive Power System Stabilizer (NPSS) was designed to improve the transient stability of grid-connected Double Fed Induction Generators (DFIGs). An online training algorithm based Elman artificial neural network was adopted to achieve adaptive control. For releasing computing burden and improving computing speed, a simplified method was used, where the calculation of jacobian matrix was replaced by the sign of itself. A simplified and generic renewable power system demonstrates the control performance contributions. The results of both dominant eigenvalue analysis and time response simulation illustrate contributions to system damping that the NPSS can make. Performance capabilities superior to those provided by Synchronous Generation (SG) with Automatic Voltage Regulator (AVR) and PSS control demonstrate that NPSS installed DFIG has better performances of system damping, voltage regulation and transient stability.

Key words: double fed induction generator, power system stabilizer, artificial neural network, system damping

中图分类号:

TP13

牛玉广, 杨巍, 李晓明, 王世林, 林忠伟. 双馈风机神经电力系统稳定器设计与仿真研究[J]. 系统仿真学报, 2017, 29(1): 162-169.

Niu Yuguang, Yang Wei, Li Xiaoming, Wang Shilin, Lin Zhongwei. Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG[J]. Journal of System Simulation, 2017, 29(1): 162-169.

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双馈风机神经电力系统稳定器设计与仿真研究

Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG

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