A modulated model-free predictive control with minimum switching losses (MSL-MMFPC) is proposed to improve the steady-state performance and reduce the switching losses for a permanent magnet synchronous motor (PMSM) drive system. Firstly, two adjacent current vectors are determined based on the predefined first-level cost function, and then, make the current vector at the next control period equal to the reference current vector by modulating the selected current vectors properly. Additionally, in order to keep optimal control performance also in the over-modulation region, a new rotating coordinate frame is used to adjust the optimal voltage vector. Then, the second-level cost function is designed to select the optimal voltage vector sequence, so that the switching of a VSI leg does not happen during the phase-current maximum, which can reduce the switching losses of the inverter. The simulation and experimental results verify the effectiveness of the proposed control method.

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Modulated Model-Free Predictive Control With Minimum Switching Losses for PMSM Drive System

A model predictive control (MPC) strategy for grid-connected power converters is proposed in this article to reduce the current ripples without using ac voltage sensors. First, a sliding-mode observer is designed to estimate the grid voltage with an adaptive compensation algorithm. Thus, grid-voltage sensors are removed. The novelty of the proposed grid-voltage observation method is that it is inherent frequency adaptive without using the accurate grid angular frequency. By implementing the MPC strategy using the estimated grid voltage, the current ripples are reduced, especially under distorted grid-voltage conditions because of the low-pass filter used in the observer. Next, to further reduce the current ripples, a double-voltage vector-based MPC strategy is proposed based on the principle of the modulated MPC. Detailed theoretical analyses are also carried out to show its effectiveness for the first time. Finally, experimental studies are carried out to verify the validity of the proposed ac voltage sensorless MPC strategy.

A Model Predictive Control Method for Grid-Connected Power Converters Without AC Voltage Sensors

The model predictive direct power control (MPDPC) has attracted significant attention due to its outstanding dynamic response and high power factor. However, the variable switching frequency makes the design of alternating current (ac) filter more challenging, and the heavy computational burden limits the application of MPDPC. This paper proposed a new cost function and four steps’ MPDPC (FSMPDPC) scheme for T-type inverters. The proposed cost function can reduce the number of division operations and does not require calculating the duty cycles of all vectors. Meanwhile, the four steps’ calculation process is divided into four steps to reduce the number of cycle calculations. The first three steps are assigned to adjust the active and reactive powers, and the neutral-point (NP) voltage is balanced in the fourth step. An experimental platform of a T-type inverter is established to demonstrate the superiorities of the proposed FSMPDPC. The results show that FSMPDPC improves the steady-state performance of the T-type inverter with lower current total harmonic distortion (THD) and lower ripples in the active and reactive powers. In addition, the proposed algorithm eases the computational burden of the digital signal processor (DSP).

Model Predictive Direct Power Control With Fixed Switching Frequency and Computational Amount Reduction

In this article, a new neural network-based virtual flux (NN-VF) estimator is proposed for sensorless control of a pulsewidth modulation rectifier under unbalanced and distorted grid conditions. In this estimator, the VF vector is reconstructed through an emulated ideal integrator. Thereafter, positive and negative sequence (PNS) VF components are extracted using an NN-based PNS components separator. A Lyapunov’s theory-based convergence analysis is performed for optimal tuning of the NN-VF estimator. Accordingly, accurate and fast estimation is achieved. A VF-based predictive direct power control (VF-PDPC) model, including the estimated VF positive sequence components, is formulated. A startup procedure is considered for smooth starting under unbalanced grid conditions. Feasibility and robustness of the developed VF-PDPC are verified by experiments. Mainly, obtained results demonstrate that the startup procedure reduces settling time and avoids over currents; the VF-PDPC achieves better current waveforms than those obtained by the conventional PDPC under unbalanced grid conditions; the NN-VF estimator presents similar dynamic performances as the second-order generalized integrator that uses grid voltages measurement.

Virtual Flux Estimation for Sensorless Predictive Control of PWM Rectifiers Under Unbalanced and Distorted Grid Conditions

Axial flux permanent magnet synchronous motors (AFPMSMs) have advantages over other motor types in terms of efficiency, vibration, and noise. However, torque ripple is an important problem for axial flux permanent magnet (AFPM) motors. One of the methods recommended as a solution to this problem is the skew operation. The skew can be applied to the magnets or the stator. However, for these motors, this process is mostly applied to magnets because the skew applied to the stator makes it difficult to manufacture. In this article, a solution to the torque ripple of an in-wheel AFPMSM for the electric vehicle is sought. For this purpose, designs, optimization, and 3-D analyzes have been carried out. This solution has been applied to magnets designed by skewing for ease of production. 3-D analyses have been carried out by applying skew of up to 40° with an angle of 5° to the magnets in the rotor. In light of the analysis, the most appropriate skew angle that does not affect motor performance has been determined. As a result of the analysis of the motor model obtained with the determined skew angle, it is observed that the torque ripple decreased by 86.71%. Finally, production has been carried out according to the analysis results obtained.

Performance Analysis and Reduction of Torque Ripple of Axial Flux Permanent Magnet Synchronous Motor Manufactured for Electric Vehicles

A voltage sensorless control of low-complexity model predictive direct power control (LC-MPDPC) for pulsewidth modulation (PWM) rectifier is proposed. The conventional LC-MPDPC adopts one or two voltage vectors during one control period, which achieve good steady-state performance and quick dynamic response. In addition, based on the mathematical model of the real system, the conventional method only requires one prediction to find the optimal voltage vector, which reduces the control complexity and computational burden. However, the use of one or two vectors during one sampling interval presents abundant current harmonics and high power ripples, and the switching frequency is variable. In order to solve these problems while preserving all the advantages of the conventional LC-MPDPC, this paper presents a novel control scheme, with the aim of operating at a constant switching frequency and obtaining an excellent steady-state performance at a low switching frequency. The proposed method is based on an optimal application of three voltage vectors in a symmetrical way, which takes advantage of advanced PWM techniques. Furthermore, the virtual flux-based control scheme is introduced to achieve voltage sensorless control. The proposed strategy is compared with the conventional MPDPC methods and its effectiveness is confirmed by both simulation and experimental results from a three-phase PWM rectifier under 1000-W operation condition.

Three-Vector-Based Low-Complexity Model Predictive Direct Power Control Strategy for PWM Rectifier Without Voltage Sensors

Reduction of the cogging torque is preferred during the design process of a permanent magnet (PM) synchronous machine (PMSM), especially in the low-speed traction applications. This article presents a dual-skew magnet technique to minimize the cogging torque in the axial-flux permanent-magnet (AFPM) motor with a yokeless and segmented armature (YASA). The theoretical expression of the cogging torque of the AFPM machine is deduced, and the advantages of the dual-skew magnet are verified by comparing with a sector shape magnet and a conventional skew magnet using the 3-D finite-element method (FEM). The simulation results show that, compared with the other two magnet topologies, the machine equipped with the dual-skew magnet shows its advantages in the reduction of cogging torque, torque ripple, and magnet eddy current loss.

Dual-Skew Magnet for Cogging Torque Minimization of Axial Flux PMSM With Segmented Stator

This paper focuses on the multi-objective optimization of an air-cored axial flux permanent magnet synchronous machine (AFPMSM) with segmented PMs. Support vector machine based on radial basis kernel function is combined with orthogonal design to create the surrogate model between output performance and design parameters of a coreless AFPMSM with segmented PMs efficiently, and genetic algorithm is used to obtain the global optimal solutions to decrease the torque ripple and increase the output torque. The effectiveness of the proposed method is verified by finite element analysis.

Multi-Objective Optimization of an Air-Cored Axial Flux Permanent Magnet Synchronous Machine with Segmented PMs based on Support Vector Machine and Genetic Algorithm

A novel strategy of model predictive direct power control (MPDPC) for PWM rectifier is proposed in this paper. Previous studies have proposed one-vector-based and two-vector-based MPDPC methods for PWM rectifier, which achieve good steady-state performance and quick dynamic responses. However, due to the limited switching states in the three-phase two-level PWM Rectifier, the sampling frequency should be high to obtain the satisfactory control performance. Besides, the switching frequency is variable, and this makes it difficult to design the filtering inductance. To deal with those problems, while maintaining all the advantages of the conventional MPDPC, this paper presents a new control strategy. The proposed MPDPC is based on an optimal application of three voltage vectors in a symmetrical way, which is known as symmetrical 3+3 vectors' sequence. The simulation and experimental results prove that, compared to conventional MPDPC, the proposed MPDPC achieves the steady-state harmonic spectrum at lower level and remains quick dynamic responses.

Three-Vector-Based Model Predictive Direct Power Control Strategy for PWM Rectifier

In this paper, the influence of rotor topology on the electromagnetic performances of dual-rotor axial-flux permanent magnet (DRAFPM) machines with modular stator (MS) and fractional slot non-overlapping winding (FSNW) is investigated by comparing four different rotor structures, including surface-mounted PM (SPM), consequent-pole PM (CPM), Halbach CPM (HCPM) and interior PM rotor (IPM) structures. In order to reduce the PM flux leakage in CPM rotor, the air-barrier introduced beside the PM is optimized by the three-dimensional finite-element method. Then, the main electromagnetic characteristics of the investigated machines, such as open-circuit airgap flux density, back-EMF, average torque, torque ripple, PM eddy-current loss and PM demagnetization are compared quantitatively. Consequently, the machine with SPM rotor accommodates the highest back-EMF and output torque; meanwhile, the CPM machine possesses the highest torque per PM volume and inductance of d-axis. Moreover, both CPM and IPM machine generate reluctance torque, and the IPM machine features larger salient pole ratio and stronger resistance to irreversible demagnetization, as well as the highest machine efficiency.

Wei Le

Papers

Three-Vector-Based Low-Complexity Model Predictive Direct Power Control Strategy for PWM Rectifier Without Voltage Sensors

Dual-Skew Magnet for Cogging Torque Minimization of Axial Flux PMSM With Segmented Stator

Multi-Objective Optimization of an Air-Cored Axial Flux Permanent Magnet Synchronous Machine with Segmented PMs based on Support Vector Machine and Genetic Algorithm

Three-Vector-Based Model Predictive Direct Power Control Strategy for PWM Rectifier

Comparative Analysis of Dual-Rotor Modular Stator Axial-Flux Permanent Magnet Machines With Different Rotor Topologies