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This article presents a flux linkage-based direct model predictive current control approach that achieves favorable performance both during steady-state and transient operation. The former is achieved by computing the optimal time instants at which a new switch position is applied to the converter. To this end, the future current behavior is not computed based on the machine inductances or inductance look-up tables; instead, flux linkage maps are utilized to predict the trajectory of the magnetic flux linkage, and subsequently of the current. This is advantageous for electric drives with noticeable magnetic nonlinearity in terms of saturation and/or cross-coupling effects. Hence, by using flux linkage maps in the prediction process, the evolution of the stator current can be calculated more accurately, enabling the controller to make better switching decisions. Moreover, the discussed predictive controller exhibits excellent dynamic performance owing to its direct control nature, i.e., the control and modulation tasks are performed in one computational stage rendering a dedicated modulation stage redundant. Three different drive systems based on permanent magnet synchronous motors are examined to demonstrate the effectiveness of the presented control approach.
Enhanced Flexible Algorithm for the Optimization of Slot Filling Factors in Electrical Machines
(2020)
The continuous development in the field of industrial automation and electric mobility has led to the need for more efficient electrical machines with a high power density. The improvement of electrical machines’ slot filling factors is one of the measures to satisfy these requirements. In recent years, this topic has aroused greater interest in the industrial sector, since the evolution of the winding technological manufacturing processes allows an economically sustainable realization of ordered winding arrangements, rather than random ones. Moreover, the manufacture of electrical machines’ windings must be preceded by an accurate design phase in which it is possible to evaluate the maximum slot filling factor obtainable for a given wire shape and for its dimensions. For this purpose, this paper presents an algorithmic approach for the evaluation of maximum slot filling factors in electrical machines under an ideal geometric premise. In particular, this algorithm has a greater degree of flexibility with respect to the algorithm approaches found in the literature, since the study has been extended to round, rectangular and hexagonal wire sections. Furthermore, the slot filling factor calculation was carried out both for standard and non-standard slots. The algorithmic approach proposed can be considered as an additional useful tool for the fast design of electrical machine windings.
Design of a Wide Input Voltage Range Current-Fed DC/DC Converter Within a Reduced Duty-Cycle Range
(2020)
A design of a current-fed push-pull converter for wide input voltage ranges within a reduced duty-cycle range is presented. Based on the converter conversion ratios of three different clamping configurations, the potential of the input clamping is shown analytical, simulative and by measurements. A differentiation is made between the four possible operating modes, which can be distinguished in the control of the semiconductors and the magnetomotive force in the reactor. By optimizing the turn ratios of both transformers, the wide input voltage range capability of the input clamped current-fed push-pull converter was improved, which has been proved by measurements on a real prototype.
Design Concept of a Wound Field Transverse Flux Machine using Soft Magnetic Composite Claw-Poles
(2020)
This paper presents the machine concept of a wound field transverse flux machine (WFTFM). In contrast to permanent magnet transverse flux machines (PMTFM), here the magneto motive force (mmf) of the rotor is generated by a current. The magnetic circuit of the WFTFM is made of soft magnetic composite (SMC) and is designed as claw-poles. Thus the magnetic circuit consists of only a few components. Together with the simple ring coils in stator and rotor, the WFTFM offers advantages in the production of electrical machines. The basic structure of the machine and a first design is described here. The results generated by numerical three-dimensional finite element analysis (FEA) are compared with those of conventional machines.
Control of multi-phase machines is a challenging topic due to the high number of controlled variables. Conventional control methods, such as field-oriented control (FOC), address this issue by introducing more control loops. This, however, increases the controller design complexity, while the tuning process can become cumbersome. To tackle the above, this paper proposes a deep deterministic policy gradient algorithm based controller that fulfills all the control objectives in one computational stage. More specifically, the proposed approach aims to learn a suitable current control policy for six-phase permanent magnet synchronous machines to simplify the commissioning of the drive system. In doing so, physical limitations of the drive system can be accounted for, while the compensation of imbalances between the two three-phase subsystems is rendered possible. After validating the training results in a controller-in-the-loop environment, test bench measurements are provided to demonstrate the effectiveness of the proposed controller. As shown, favorable steady-state and dynamic performance is achieved that is comparable to that of FOC. Therefore, as indicated by the presented results, reinforcement learning-based control approaches for multi-phase machines is a promising research area.
This paper presents a current control approach for permanent magnet synchronous machines (PMSMs) using the deep reinforcement learning algorithm deep deterministic policy gradient (DDPG). The proposed method is designed by examining different training setups regarding the reward function, the observation vector, and the actor neural network. In doing so, the impact of the different design factors on the steady-state and dynamic behavior of the system is assessed, thus facilitating the selection of the setup that results in the most favorable performance. Moreover, to provide the necessary insight into the controller design, the entire path from training the agent in simulation, through testing the control in a controller-in-the-loop (CIL) environment, to deployment on the test bench is described. Subsequently, experimental results are provided, which show the efficacy of the presented algorithm over a wide range of operating points. Finally, in an attempt to promote open science and expedite the use of deep reinforcement learning in power electronic systems, the trained agents, including the CIL model, are rendered openly available and accessible such that reproducibility of the presented approach is possible.
This paper presents the hardware design, software architecture and workflow for rapid control prototyping intended for a wide-range of power electronics based systems. The proposed system is especially useful for computationally intensive algorithms and applications with high demands on the number of required measurements and gate signals. The focus is on a heterogeneous system architecture with minimized latency and jitter as well as a high signal integrity. The software architecture enables a simple, fast and performance driven implementation. A combination of carrier board and exchangeable interfacing adapter boards allows to control a wide range of power electronics applications and converters, i.e., starting with a single switching device, converters with one or several voltage levels and one or several phase legs, up to large modular multilevel converters for grid-tied connection or variable speed drives.
In this paper, a flux linkage-based direct model predictive current control approach is presented for small permanent magnet synchronous motor (PMSM) drives. The method aims to minimize the current ripples at steady state by deciding on the optimal switching instant, while exhibiting fast dynamic behavior during transients. To this end, the future trajectory of the stator current is not computed based on the machine inductances or inductance look-up tables, but on the changes of the magnetic flux linkage by utilizing flux linkage maps. As shown, the proposed method can be particularly advantageous for electric drives with a noticeable nonlinearity in terms of saturation and/or cross-coupling effects since it allows for a significantly increased prediction accuracy, which leads to an improved steady-state performance as indicated by the reduced current distortions.
This paper presents a comparative simulation analysis of an Interior Permanent Magnet Synchronous Machine (IPMSM) performance once fed by a traditional three-phase two-level inverter and then using a three-phase five-level Cascaded H-Bridges Multilevel Inverter (CHBMI). For this purpose, an enhanced mathematical model of the IPMSM, that take into account simultaneously saturation, cross-coupling, spatial harmonics and iron loss effects, has been employed. Furthermore, two different PWM modulation strategies have been considered. The study was conducted for several working conditions, evaluating the impact of CHBMI adoption on the IPMSM performance in terms of improved efficiency and torque ripple reduction.