Current Collapse Conduction Losses Minimization in GaN Based PMSM Drive

Abstract

The ever-increasing demands on the efficiency and power density of power electronics converters lead to the replacement of traditional silicon-based components with new structures. One of the promising technologies represents devices based on Gallium-Nitride (GaN). Compared to silicon transistors, GaN semiconductor switches offer superior performance in high-frequency converters, since their fast switching process significantly decreases the switching losses. However, when used in hard-switched converters such as voltage-source inverters (VSI) for motor control applications, GaN transistors increase the power dissipated due to the current conduction. The loss increase is caused by the current-collapse phenomenon, which increases the dynamic drain-source resistance of the device shortly after the turn-on. This disadvantage makes it hard for GaN converters to compete with other technologies in electric drives. Therefore, this paper offers a purely software-based solution to mitigate the negative consequences of the current-collapse phenomenon. The proposed method is based on the minimum pulse length optimization of the classical 7-segment space-vector modulation (SVM) and is verified within a field-oriented control (FOC) of a three-phase permanent magnet synchronous motor (PMSM) supplied by a two-level GaN VSI. The compensation in the control algorithm utilizes an offline measured look-up table dependent on the machine input power.

Fault Diagnosis of Inter-Turn Fault in Permanent Magnet-Synchronous Motors Based on Cycle-Generative Adversarial Networks and Deep Autoencoder

“…Cycle GAN is used to expand the PMSM sample set, and the expanded sample set is used to train the feature extraction network DAE and the fault classifier. The validity of Cycle GAN to sample expansion is verified by analyzing the correlation between generated and actual samples [22,23]. The improvement effect of this sample expansion scheme on fault feature classification is verified by analyzing the accuracy of the DAE feature extraction network and fault classifier to fault classification.…”

Control of Permanent Magnet Synchronous Motors for Track Applications

“…3.1.3. Operating a PMSM Together in a Fundamental FOC (Field-Oriented Control) Loop GaN (Generative adversarial network)-based converter's losses brought generated by the current-collapse phenomenon but also their prevention, respectively, were explored, according to [18]. It became clear that the executive development of on-states resistance, which relies on the modulation scheme being employed and the length of the pulse, is responsible for the extra losses.…”

“…It became clear that the executive development of on-states resistance, which relies on the modulation scheme being employed and the length of the pulse, is responsible for the extra losses. Following that, the minimum pulses for the GaN (Generative adversarial network) gates drivers are limited in order to regulate the SVPWM (Space vector pulse width modulation) (Pulse Width Modulation) of either a three-phase Voltage Source Inverter (VSI) operating a PMSM together in a fundamental FOC (Field-Oriented Control) loop [18], see Figure 3. In this study, it was discovered that while the length of the pulse limiting-induced modulated voltage reduces the current-collapses losses, it also introduces current distortion, which increases machine damage.…”

“…Additionally, it was discovered through experimentation that perhaps the converter inputs power affects the ideal restricting duty cycle value. In order to fix the duty cycle limit with regard to the real VSI inputs power determined from either the motors currents or voltages, another compensation mechanism was added to the motors control algorithms [18].…”