Synchronous Reluctance Motor Parameter and State Estimation Using Extended Kalman Filter and Current Derivative Measurement

Mynář, Zbyněk; Václavek, Pavel; Blaha, P.

doi:10.1109/tie.2020.2973897

Cited by 44 publications

(24 citation statements)

References 29 publications

(43 reference statements)

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“…The literature has paid much attention to magnetic saturation [13] [14] and iron losses [15] [16] [17] for SynREL machines. Moreover, residual magnetic flux has been mentioned in self excitation phenomenon studies in generator applications.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Model of Residual Magnetism for Synchronous Reluctance Machine Control

Schuller

Gauthier

Delpoux

et al. 2022

IEEE Trans. Ind. Electron.

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Section: Introductionmentioning

confidence: 99%

Dynamical Model of Residual Magnetism for Synchronous Reluctance Machine Control

Schuller

Gauthier

Delpoux

et al. 2022

IEEE Trans. Ind. Electron.

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“…Since offline methods may require system deactivation, they do not typically represent a suitable solution for a wide range of applications, including UPS systems. Hence, additional emphasis has been given to online estimation methods especially in grid-connected converters [6][7][8][9], rectifiers [10][11][12][13] and electrical machine drives [14][15][16][17][18][19][20] applications. In [6], an estimation method using a prediction error algorithm and two specific discrete-time models is proposed to determine the parameters of an LCL filter in a grid-connected converter.…”

Section: Introductionmentioning

confidence: 99%

“…RLS-based estimation methods are also proposed in [10] and [19] to respectively estimate an inductive rectifier filter and the inductances of a PMSM. Parameter estimation methods based on Extended Kalman Filters (EKF) have also been widely investigated, especially for electrical machines [17,18,20]. Finally, in [22], the parameters of an inverter LC filter are identified by estimating the instantaneous charge transferred from the inverter to a power grid.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, its potential has been shown in several works regarding not only filter parameter estimation [2,12,24,26] but also current/voltage harmonic components identification [27,28] and flux estimation in electrical machines [23], which have reported a good estimation accuracy. Furthermore, according to [5,25], ADALINE-based parameter estimation techniques typically require lower computational burden and present faster convergence than more conventional estimation methods, including those based on EKFs [17,18,20] and MRAS [11,[14][15][16]. Thus, its low computational complexity and good estimation accuracy turn ADALINE into a highly promising solution to be used with control approaches that simultaneously present high computational burden and whose performance considerably depends on an accurate system model, which is precisely the case of Model Predictive Control (MPC).…”

Section: Introductionmentioning

confidence: 99%

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Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

et al. 2022

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Uninterruptible Power Supplies (UPS) represent the key technology to continuously feed and protect a wide range of critical applications in electric power systems. Due to their continuous operation, UPS components degrade over time, including their filtering elements, leading to decreased filtering capabilities. With these deviations, UPS performance is typically reduced. Moreover, if filter parameter deviations are not considered in the control system, performance degradation can be further increased and the critical load can be seriously compromised. This is especially important with Model Predictive Control (MPC), which heavily relies on system parameters accuracy. Thus, control performance optimization using estimated filter parameters in UPS systems can be extremely important. Nevertheless, this has not yet been covered in the literature for UPS systems. In light of these facts, this paper proposes a new mechanism that, by using online estimated filter parameters, optimizes the performance of an MPC strategy, in a UPS system. In the scope of this optimization mechanism, an estimation method that enables parameter identification and control optimization not only in balanced but also in highly unbalanced filter conditions (rarely studied in the literature) is proposed. Experimental results demonstrate the accuracy of the proposed estimators and the effectiveness of the proposed control performance optimization mechanism. Under severe filter parameter variations, the proposed performance optimization scheme enabled to reduce the degradation (caused by filter variation) of the grid current and load voltage THD by 29.41 % and 91.60 %, respectively. Furthermore, the degradation of the RMS load voltage value was also significantly reduced by 97.98 %.INDEX TERMS model predictive control, online inductance and capacitance estimation, power quality, real-time control performance optimization, uninterruptible power supply.

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“…In some work like [12], the motor model parameters are estimated using the Disturbance Observer (DOB) as a torque sensor. Another option is the technique studied in [13], where the authors use an extended Kalman filter and the measurement of the current derivative for the estimation of parameters for Synchronous Reluctance Motor and demonstrate their results experimentally. However, errors remain relatively significant on some parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Motors by Cuckoo Search Using Steady-State Relations

et al. 2021

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The direct current (DC) motors are widely used; therefore, they are subject to multiple studies, different control techniques or analyses require a dynamic DC motor model. The parameters are needed to complete the model, which can be challenging to obtain. Therefore, multiple parametric estimation techniques have been developed. This paper presents a metaheuristic cuckoo search algorithm modified for motors as a parametric estimation tool. A cost function is based on the current and velocity error obtained when an input voltage step is applied to the motor. The main difference with similar works is that we used the steady-state equations to determine the parameters. The algorithm proposed is compared with the Steiglitz-McBride and the original cuckoo search algorithms to evaluate its performance objectively. Simulated and experimental results show that the algorithm proposed can calculate the parameters with better accuracy than the original cuckoo search and Steiglitz-McBride. The modifications made to the original algorithm of the cuckoo search allowed finding the values of the parameters motor with a root mean square error of less than 0.1% for signals obtained with simulation and less than 1% for real signals sampled at 0.001 s.

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Synchronous Reluctance Motor Parameter and State Estimation Using Extended Kalman Filter and Current Derivative Measurement

Cited by 44 publications

References 29 publications

Dynamical Model of Residual Magnetism for Synchronous Reluctance Machine Control

Dynamical Model of Residual Magnetism for Synchronous Reluctance Machine Control

Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

Parameter Identification of Motors by Cuckoo Search Using Steady-State Relations

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