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基于反电动势与磁链正交性的异步电机电压模型积分改进算法
张杰 ; 柴建云 ; 孙旭东 ; 陆海峰 ; Zhang Jie ; Chai Jianyun ; Sun Xudong ; Lu Haifeng
2016-03-30 ; 2016-03-30
关键词磁链观测 电压模型 正交性 积分算法 异步电机 Flux estimator voltage model orthogonality integral algorithm induction motor TM343
其他题名An Improved Voltage Model Integral Algorithm of Induction Motors based on the Orthogonality between Back EMF and Flux
中文摘要在异步电机高性能控制方案中,电机磁链的准确观测是其核心问题之一。基于电压模型的磁链观测算法因具有中高速区参数鲁棒性高等优点而得到了广泛应用。然而,在实际使用时,电压模型的纯积分运算会因积分初始时刻的问题和积分量中微小的直流偏置而导致错误结果,进而影响了电机高性能控制的实现。针对该问题,本文在详细分析反电动势与磁链的各组成分量的基础上,提出一种基于反电动势与磁链正交性的积分改进算法。该算法将基于正交性获得的信息通过PI调节器得到反电动势的校正分量,再通过校正分量修正反电动势进而得到正确的积分结果。理论分析表明,在一定条件下,积分结果中的直流偏置可以完全得到消除。本文通过仿真和实验,验证了该改进积分方法良好的性能,也验证了关于完全消除积分结果中的直流偏置的理论分析的正确性。; Accurate flux estimation is one of the key issues in the high-performance induction motor drives. Among all the flux estimation algorithms, one method, on the basis of voltage model, is widely used due to its parameter robustness in middle and high speed range. However, the DC drift and initial value problems caused by the pure integration in this method could lead to the incorrect results and furthermore, impedes the high-performance drives. To overcome this problem, each component of the back EMF and flux are analyzed first and an improved integration algorithm based on the orthogonality of back EMF and flux is proposed in this paper. In this algorithm, the information from the orthogonality is passed to PI controllers and the correction value of EMF is taken from the output of PI controllers and correct result is obtained. Theoretically, the DC drift in the integration results could be completely eliminated. Simulation and experimental results verify both the good performance of this algorithm and the theoretical conclusion.
语种中文 ; 中文
内容类型期刊论文
源URL[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/142505]  
专题清华大学
推荐引用方式
GB/T 7714
张杰,柴建云,孙旭东,等. 基于反电动势与磁链正交性的异步电机电压模型积分改进算法[J],2016, 2016.
APA 张杰.,柴建云.,孙旭东.,陆海峰.,Zhang Jie.,...&Lu Haifeng.(2016).基于反电动势与磁链正交性的异步电机电压模型积分改进算法..
MLA 张杰,et al."基于反电动势与磁链正交性的异步电机电压模型积分改进算法".(2016).
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