Deadbeat Predictive Current Control of PMSM with Nonlinear Parameters Online Identification

Yin, Kaixuan; Gao, Lin; Wen-hua, FU; Feng, Zuren

doi:10.1109/icet51757.2021.9450937

Cited by 2 publications

(3 citation statements)

References 13 publications

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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%

“…In [11], an estimation method based on a Model Reference Adaptive System (MRAS) approach is proposed to identify the inductances of a rectifier input filter. MRAS-based methods are also investigated in [14][15][16] and [21] to estimate the inductances of a Permanent Magnet Synchronous Motor (PMSM) and the filter parameters of an inverter LC filter, respectively. In [9], an estimation method based on the Recursive Least-Squares (RLS) algorithm is proposed to identify the filter inductances of paralleled gridconnected converters.…”

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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