The topology selection plays a key role in minimizing the losses and improving the output waveform quality of an inverter. In addition, increasing the switching frequency of an inverter help to reduce the size of EMI ...
The topology selection plays a key role in minimizing the losses and improving the output waveform quality of an inverter. In addition, increasing the switching frequency of an inverter help to reduce the size of EMI filters resulting in power density improvement. Use of wide-bandgap device (like SiC devices) enables high switching frequency operation in power electronic converters. However, due to low gate charge and small junction capacitance of SiC devices, the SiC-based inverter is more likely to be influenced by side-effects of fast switching transition like undesired ringing, larger voltage overshoot and mistriggerig due to miller-effect. Parasitic inductances of multiple power loops and gate loops within the inverter leg play a key role in these undesired effects, however, a proper design of PCB-layout can reduce these effects. In this paper, a comparison is conducted between a T-type and 2-level inverter topologies for motor drive applications. Furthermore, a placement configuration of components along with the PCB layout are proposed for a T-type leg to improve its switching transition behavior by reducing the power and gate loop parasitic inductances. The parasitic inductances of proposed configuration are estimated using FEA simulation, and the estimated values are used to simulate the switching transition behavior of a T-type leg. Finally, a Double Pulse Test (DPT) is executed to demonstrate the accurate switching transition behaviour of a T-type leg which is designed with the proposed PCB layout and component placement.
It is imperative for energy systems to reduce their environmental footprint in a cost-efficient manner, for which renwable energy sources (RES) and complementary technologies become desirable options to achieve said t...
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This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel sta...
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ISBN:
(数字)9798350304053
ISBN:
(纸本)9798350304060
This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.
The equilibrium between dc bus voltage and ac bus frequency(Udc-f equilibrium)is the algorithm core of unified control strategies for ac-dc interlinking converters(ILCs),because the equilibrium implements certain ***,...
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The equilibrium between dc bus voltage and ac bus frequency(Udc-f equilibrium)is the algorithm core of unified control strategies for ac-dc interlinking converters(ILCs),because the equilibrium implements certain ***,what the mechanism is has not been explicitly explored,which hinders further studies on unified *** paper reveals that the state-space model of a Udc-f equilibrium controlled ILC is highly similar to that of a shaft-to-shaft machines *** a detailed mechanism is dis-covered and named“virtual shaft-to-shaft machine(VSSM)”mechanism.A significant feature of VSSM mechanism is self-synchro-nization without current sampling or ac voltage sampling.
This letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strat...
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Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynam...
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Non-Abelian physics, originating from noncommutative sequences of operations, unveils novel topological degrees of freedom for advancing band theory and quantum computation. In photonics, significant efforts have been...
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Here we report an intelligent soft robotic gripper enabled by the integration of an ultrasonic remote sensor and triboelectric sensors. Due to the noncontact distance sensing ability, the ultrasonic sensor is used to ...
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Intelligent reflecting surface (IRS) can significantly improve unmanned aerial vehicle (UAV) communication qualities by reconfiguring or even optimizing wireless propagation environments. For active IRS (AIRS) enabled...
Intelligent reflecting surface (IRS) can significantly improve unmanned aerial vehicle (UAV) communication qualities by reconfiguring or even optimizing wireless propagation environments. For active IRS (AIRS) enabled UAV-based mobile wireless networks, we aim to maximize the sum transmission rates of all IoT devices (IoTDs), by jointly optimizing the AIRSs’ phase shifts and amplification factors and UAV trajectory under the discrete IRS model. First, we formulate a sum transmission rate maximization problem for IoTDs, where all IoTDs can transmit data simultaneously by integrating the non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) techniques. The formulated problem is non-convex because the coupled phase shifts and amplification factors are both discrete variables based on the discrete IRS model. Hence, we develop multi-agent deep reinforcement learning (MADRL) based schemes by integrating the deep deterministic policy gradient (DDPG) and Dueling Deep Q Network (Dueling DQN) techniques to derive the UAV trajectory and the amplification factors and phase shifts of AIRSs, respectively. Finally, we validate and evaluate the performances improvements of our developed MADRL-based schemes through extensive simulations.
Logic diagnosis is essential for improving reliability and yield. In conventional diagnosis methods, although various methods are proposed to enhance the accuracy and resolution of logic diagnosis, there are still dia...
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