A novel parameter estimation method for RPRI radar

2021 ◽  
Author(s):  
S. Wu ◽  
S. Zhang
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Parameter Estimation Method

scholarly journals Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-wei Yang ◽  
Man-feng Dou ◽  
Zhi-yong Dai
Keyword(s):  
Parameter Estimation ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Estimation Method ◽  
Trust Region ◽  
Fault Tolerant Control ◽  
Trust Region Algorithm ◽  
Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

Robust parameter estimation method for bilinear model

2015 ◽  
Author(s):  
Mohd Isfahani Ismail ◽  
Hazlina Ali ◽  
Sharipah Soaad S. Yahaya
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Bilinear Model ◽  
Parameter Estimation Method ◽  
Robust Parameter ◽  
Robust Parameter Estimation

scholarly journals MODEL DECOMPOSITION BASED ABNORMAL PARAMETER ESTIMATION FOR DISTILLATION COLUMN

2018 ◽  
Vol 48 (2) ◽  
pp. 81-87
Author(s):  
W. D. TIAN ◽  
S. L. SUN
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Model ◽  
Distillation Column ◽  
Estimation Method ◽  
Matrix Analysis ◽  
Physical Parameters ◽  
Time Saving ◽  
Model Decomposition ◽  
Parameter Estimation Method ◽  
Occurrence Matrix

Parameter estimation method can produce useful physical parameters in finding abnormal causes, but nonlinear model makes this method computationally intensive and non-robust for distillation scenario. In this paper, we propose a model decomposition based parameter estimation method for distillation column diagnosis purposes. Nonlinear first principles dynamic model is divided into some disjoint submodels through occurrence matrix analysis. The whole model is used to monitor distillation process and the submodel that gives the highest contribution to the generated residual is selected to perform abnormal parameter estimation. Application results from stripping tower in the popular Tennessee Eastman challenge problem show that the model decomposition based diagnosis scheme is more time-saving and robust than pure nonlinear model based scheme.

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