感应电动机模型参数在线辨识的UKF算法
针对高阶非线性动态系统参数估计的非线性特征，介绍了无味卡尔曼滤波（UKF）算法。在给出了UKF的算法描述的基础上，从一般意义上讨论了无味变换（UF）仅用有限的参数来近似随机变量的概率统计特征，避免了传统的通过线性化来估计非线性系统而带来的误差，进而将该算法用于电力系统感应电动机动态负荷模型的参数估计。算例利用某电网同步相量测量（PMU）采集数据，利用所提算法实时跟踪模型参数，结果表明该算法能够实时有效地辨识出感应电动机负荷模型的参数，有望在实际工程中得到应用。...
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| Published in | 电力系统保护与控制 Vol. 40; no. 24; pp. 84 - 88 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
西藏职业技术学院,西藏拉萨850000
2012
东南大学电气工程学院,江苏南京210096%东南大学电气工程学院,江苏南京,210096 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
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| Abstract | 针对高阶非线性动态系统参数估计的非线性特征，介绍了无味卡尔曼滤波（UKF）算法。在给出了UKF的算法描述的基础上，从一般意义上讨论了无味变换（UF）仅用有限的参数来近似随机变量的概率统计特征，避免了传统的通过线性化来估计非线性系统而带来的误差，进而将该算法用于电力系统感应电动机动态负荷模型的参数估计。算例利用某电网同步相量测量（PMU）采集数据，利用所提算法实时跟踪模型参数，结果表明该算法能够实时有效地辨识出感应电动机负荷模型的参数，有望在实际工程中得到应用。 |
|---|---|
| AbstractList | TM71; 针对高阶非线性动态系统参数估计的非线性特征,介绍了无味卡尔曼滤波(UKF)算法.在给出了UKF的算法描述的基础上,从一般意义上讨论了无味变换(UF)仅用有限的参数来近似随机变量的概率统计特征,避免了传统的通过线性化来估计非线性系统而带来的误差,进而将该算法用于电力系统感应电动机动态负荷模型的参数估计.算例利用某电网同步相量测量(PMU)采集数据,利用所提算法实时跟踪模型参数,结果表明该算法能够实时有效地辨识出感应电动机负荷模型的参数,有望在实际工程中得到应用. 针对高阶非线性动态系统参数估计的非线性特征，介绍了无味卡尔曼滤波（UKF）算法。在给出了UKF的算法描述的基础上，从一般意义上讨论了无味变换（UF）仅用有限的参数来近似随机变量的概率统计特征，避免了传统的通过线性化来估计非线性系统而带来的误差，进而将该算法用于电力系统感应电动机动态负荷模型的参数估计。算例利用某电网同步相量测量（PMU）采集数据，利用所提算法实时跟踪模型参数，结果表明该算法能够实时有效地辨识出感应电动机负荷模型的参数，有望在实际工程中得到应用。 |
| Author | 杨自群 丁涛 |
| AuthorAffiliation | 西藏职业技术学院,西藏拉萨850000 东南大学电气工程学院,江苏南京210096 |
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| Author_FL | YANG Zi-qun DING Tao |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Unscented Kalman Filter algorithm for on-line identification of parameters of induction motor model |
| DocumentTitle_FL | Unscented Kalman Filter algorithm for on-line identification of parameters of induction motor model |
| EndPage | 88 |
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| Keywords | 在线辨识 无味卡尔曼滤波 无味变换 非线性估计 感应电动机 |
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| Notes | YANG Zi-qun, DING Tao （1. Xizang Vocational and Technical College, Lasa 850000, China; 2. School of Electrical Engineering, Southeast University, Nanjing 210096, China） Unscented Kalman Filter; unscented transformation; induction motor; on-line identification; nonlinear estimation For the nonlinear characteristics of parameter estimation for high-order nonlinear dynamic systems, the Unscented Kalman Filter （UKF） algorithm is introduced. The UKF algorithm description is provided and the probability and statistics features of Unscented Transformation （UT） which uses limited parameters to approximate the random variables are discussed. Also, the error in the traditional estimation by linearization of nonlinear system is avoided. The algorithm is applied to the parameter estimation of induction motor dynamic load model in power systems. Results of case study clearly indicate that the algorithm can quickly and efficiently identify the parameters of this induction motor dynamic load model, and is expected to be imple |
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| PublicationTitle | 电力系统保护与控制 |
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| PublicationTitle_FL | Power System Protection and Control |
| PublicationYear | 2012 |
| Publisher | 西藏职业技术学院,西藏拉萨850000 东南大学电气工程学院,江苏南京210096%东南大学电气工程学院,江苏南京,210096 |
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| Snippet | 针对高阶非线性动态系统参数估计的非线性特征，介绍了无味卡尔曼滤波（UKF）算法。在给出了UKF的算法描述的基础上，从一般意义上讨论了无味变换（UF）仅用有限的参数来近似... TM71; 针对高阶非线性动态系统参数估计的非线性特征,介绍了无味卡尔曼滤波(UKF)算法.在给出了UKF的算法描述的基础上,从一般意义上讨论了无味变换(UF)仅用有限的参数来近似... |
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| SubjectTerms | 在线辨识 感应电动机 无味卡尔曼滤波 无味变换 非线性估计 |
| Title | 感应电动机模型参数在线辨识的UKF算法 |
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