Robust Predictive Control for Direct-Driven Surface-Mounted Permanent-Magnet Synchronous Generators Without Mechanical Sensors

Abdelrahem, Mohamed; Hackl, Christoph M.; Zhang, Zhenbin; Kennel, Ralph

doi:10.1109/tec.2017.2744980

Cited by 96 publications

(43 citation statements)

References 43 publications

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“…In order to improve the robustness against the parameter perturbation, a composite integral terminal SMO is designed. Taking Substituting (17) into (16), the cost function is written as:…”

Section: Design Of the Rnpccmentioning

confidence: 99%

“…In [15], a composite predictive control method based on stator current and a disturbance observer is developed, which can achieve perfect current control performance of the PMSM with model parameter mismatch. In [16], a simple disturbance observer is designed to increase the robustness of the proposed deadbeat predictive control algorithm against parameter uncertainties of the Permanent Magnetic Synchronous Generator (PMSG). In [17], a robust fault-tolerant predictive current control algorithm is proposed based on a composite observer, which can enhance robustness against parameter perturbation and permanent magnet demagnetization by adding compensation voltage.…”

Section: Introductionmentioning

confidence: 99%

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Robust Nonlinear Predictive Current Control Techniques for PMSM

Lyu

Luo

et al. 2019

Energies

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This paper proposes a robust nonlinear predictive current control (RNPCC) method for permanent magnet synchronous motor (PMSM) drives, which can optimize the current control loop performance of the PMSM system with model parameter perturbation. First, the disturbance caused by parameter perturbation was considered in the modeling of PMSM. Based on this model, the influence of parameter perturbation on the conventional predictive current control (PCC) was analyzed. The composite integral terminal sliding mode observer (SMO) was then designed to estimate the disturbance caused by the parameter perturbation in real time. Finally, a RNPCC method is developed without relying on the mathematical model of PMSM, which can effectively eliminate the influence of parameter perturbation by injecting the estimated disturbance value. Simulations and experiments verified that the proposed RNPCC method was able to remove the current error caused by the parameter perturbation during steady state operation.

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“…In order to improve the robustness against the parameter perturbation, a composite integral terminal SMO is designed. Taking Substituting (17) into (16), the cost function is written as:…”

Section: Design Of the Rnpccmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Nonlinear Predictive Current Control Techniques for PMSM

Lyu

Luo

et al. 2019

Energies

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“…A variety of speed and position estimation methods have been proposed for permanent-magnet synchronous machines (PMSMs) and induction machines (IMs), and recently they were applied successfully to SMPMSGs/DFIGs. The well-known observers in the literature include the following: phase-locked loop (PLL) [13][14][15], model reference adaptive system (MRAS) [16][17][18][19][20][21][22], sliding-mode observers [23][24][25], extended Kalman filter (EKF) [26][27][28], unscented Kalman filter (UKF) [29][30][31], and others. Due to the simplicity, ease of implementation, and direct physical interpretation, MRAS-based observers have received increased interest from researchers and engineers.…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

et al. 2020

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In micro-grid systems, wind turbines are essential power generation sources. The direct-driven surface-mounted permanent-magnet synchronous generators (SMPMSGs) in variable-speed wind generation systems (VS-WGSs) are promising due to their high efficiency/power density and the avoidance of using a gearbox, i.e., regular maintenance and noise are averted. Usually, the main goal of the control system for SMPMSGs is to extract the maximum available power from the wind turbine. To do so, the rotor position/speed of the SMPMSG must be known. Those signals are obtained by the help of an incremental encoder or speed transducer. However, the system reliability is remarkably reduced due to the high failure rate of these mechanical sensors. To avoid this problem, this paper presents a model reference adaptive system with finite-set (MRAS-FS) observer for encoderless control of SMPMSGs in VS-WGSs. The motif of the presented MRAS-FS observer is taken from the direct-model predictive control (DMPC) principle, where a certain number of rotor position angles are utilized to estimate the stator flux of the SMPMSG. Subsequently, a new optimization criterion (also called quality or cost function) is formulated to select the best rotor position angle based on minimizing the error between the estimated and reference value of the stator flux. Accordingly, the traditional fixed-gain proportional-integral regulator generally employed in the classical MRAS observers is not needed. The proposed MRAS-FS observer is validated experimentally, and its estimation response has been compared with the conventional MRAS observer under different conditions. In addition to that, the robustness of the MRAS-FS observer is tested at mismatches in the parameters of the SMPMSG.

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“…Since predictive control is based on a system model, its performance strongly depends on the model parameter accuracy [13][14][15][16], and for PMSMs especially on the rotor flux [17]. High temperatures and currents, operating under strong field-weakening conditions, mechanical stresses, magnet cracks and mechanical imperfections may cause (partial) demagnetization [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation of Surface PMSMs with Demagnetization Defect under Predictive Current Control

et al. 2019

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To control the current of a surface mounted permanent magnet synchronous machine fed by a two-level voltage source inverter, a large variety of control algorithms exists. Each of these controllers performs differently concerning dynamic performance and control- and voltage quality, but also concerning sensitivity to demagnetization faults. Therefore, this paper investigates the performance degradation of three advanced predictive controllers under a partial demagnetization fault. The three predictive controllers are: finite-set model based predictive control, deadbeat control, and a combination of both previous algorithms. To achieve this goal, the three predictive controllers are first compared under healthy conditions, and afterwards under a partial demagnetization fault. A PI controller is added to the comparison in order to provide a model-independent benchmark. Key performance indicators, obtained from both simulations and experimental results on a 4 kW axial flux permanent magnet synchronous machine with yokeless and segmented armature topology, are introduced to enable a quantification of the performance degradation of the controllers under a demagnetization fault. A general conclusion is that the deadbeat controller shows superior control quality, even under partial demagnetization.

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Robust Predictive Control for Direct-Driven Surface-Mounted Permanent-Magnet Synchronous Generators Without Mechanical Sensors

Cited by 96 publications

References 43 publications

Robust Nonlinear Predictive Current Control Techniques for PMSM

Robust Nonlinear Predictive Current Control Techniques for PMSM

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

Performance Degradation of Surface PMSMs with Demagnetization Defect under Predictive Current Control

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