基于预测控制的微电网SMES系统涡流损耗抑制方法

针对传统PWM调制方法应用于超导磁场储能（superconducting magnetic energy storage,SMES）系统中造成的涡流损耗问题,本文提出一种基于预测控制的微电网SMES系统涡流损耗抑制方法。首先,建立了SMES系统离散域预测模型,并以VSC网测电流和母线电容电压为状态变量,推导出了SMES系统的价值函数评估方法。进而,对比了PWM调制和预测控制时直流电流谐波分布规律,并对此时超导线圈中存在的涡流损耗加以评估。最后,基于50 k W样机对SMES系统预测控制方法的可行性进行验证分析,得出高幅值谐波分量将产生高涡流损耗的一般性关系,验证了预测控制方法较PWM调制在抑制...

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Bibliographic Details
Published in电测与仪表 Vol. 54; no. 12; pp. 65 - 70
Main Author 宋文峰 李春平 杨万清 栾敬钊 徐殿国
Format Journal Article
LanguageChinese
Published 国家电网辽宁省电力有限公司,辽宁 大连,116001%哈尔滨工业大学 电气工程学院,哈尔滨,150000 2017
Subjects
微电网
涡流损耗
超导磁场储能
预测控制
超导磁场储能
涡流损耗
micro-grid
eddy current loss
predictive control
预测控制
微电网
super-conducting magnetic energy storage
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ISSN1001-1390

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Summary:针对传统PWM调制方法应用于超导磁场储能（superconducting magnetic energy storage,SMES）系统中造成的涡流损耗问题,本文提出一种基于预测控制的微电网SMES系统涡流损耗抑制方法。首先,建立了SMES系统离散域预测模型,并以VSC网测电流和母线电容电压为状态变量,推导出了SMES系统的价值函数评估方法。进而,对比了PWM调制和预测控制时直流电流谐波分布规律,并对此时超导线圈中存在的涡流损耗加以评估。最后,基于50 k W样机对SMES系统预测控制方法的可行性进行验证分析,得出高幅值谐波分量将产生高涡流损耗的一般性关系,验证了预测控制方法较PWM调制在抑制涡流损耗特性方面的优越性。
Bibliography:Song Wenfeng1, Li Chunping1 , Yang Wanqing1 , Luan Jingzhao1 , Xu Dianguo1 （1. National Grid Liaoning Power Company, Dalian 116001, Liaoning, China. 2. School of Electrical Engineering, Harbin Institute of Technology, Harbin 150000, China）
23-1202/TH
micro-grid, super-conducting magnetic energy storage, eddy current loss, predictive control
In view of the eddy current losses caused by conventional PWM modulation method applied in supercon- ducting magnetic energy storgge （SMES） system, this paper proposes a micro-grid SMES based on predictive control method for suppressing eddy current losses in system. Firstly, a discrete forecasting model for SMES system is estab- lished, based on the VSC web bus capacitor voltage and measuring current for state variables, SMES system evaluation method of value function is derived. Furthermore, when comparing PWM modulation and predictive control direct cur- rent harmonic distribution of superconducting coil in eddy current loss and is assessed. Finally, SMES based on the 50 k
ISSN:1001-1390