This paper presents control system design, implementation, and experimental validation of a single-stage 400 W, 200 kHz solar photovoltaic (PV) microinverter using hardware-in-the-loop (HIL) and hardware testing. The ...
This paper presents control system design, implementation, and experimental validation of a single-stage 400 W, 200 kHz solar photovoltaic (PV) microinverter using hardware-in-the-loop (HIL) and hardware testing. The selected circuit topology is based on a Gallium Nitride (GaN) direct-matrix based dual active bridge (DAB) converter with a low voltage active power decoupler (APD) circuit. Control performance is verified, smart-grid compatibility is tested, and circuit operation is confirmed. Controller HIL (CHIL) is shown to aid in a complex power electronics system design by 1) enabling detailed control development prior to hardware implementation, 2) expanding the use of automated testing, and 3) increasing confidence in control performance prior to prototype testing. Altogether, these factors make HIL a valuable tool in complex power electronic designs.
Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due ...
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Strongly Connected Components (SCCs) play a crucial role in understanding the structural properties of directed graphs, with a plethora of applications across various domains including engineering, computer science an...
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We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in colli...
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios.
Although consortium blockchain has an identification mechanism, the captured internal clients are potentially threatening internal blockchain nodes. Internal Distributed Denial-of-Service (DDoS) attacks threaten the s...
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Electric vehicles are attracting great attention with their 48V electrification system. In this paper, the design of a two-stage 400V/48V automotive DC-DC converter based on PCB magnetics is presented. A two-phase buc...
Electric vehicles are attracting great attention with their 48V electrification system. In this paper, the design of a two-stage 400V/48V automotive DC-DC converter based on PCB magnetics is presented. A two-phase buck converter with a coupled inductor is used as the first stage to perform voltage regulation and guarantee good load transient performance. The second stage is an LLC resonant converter with a matrix transformer which provides isolation and behaves as a step-down DC transformer (DCX). The switching frequency is pushed to 500kHz to help reduce converter size and weight. By pushing the operation to high frequencies, the windings of the transformer of the LLC converter and those of the coupled inductor of the buck converter are integrated and built into the PCB. This not only improves the power density, but also minimizes the use of labor-intensive components/processes to enable truly automated manufacture. The thermal management for such converters is especially challenging due to the harsh environments it is situated in. This is accomplished using a custom-engraved aluminum baseplate to provide low thermal resistivity and increase converter reliability. The converter can achieve an efficiency of 97% and a power density of 5 kW/L.
The standard flickermeter (voltage flickermeter) algorithm commonly used for the evaluation of flicker severity in current electromagnetic compatibility standards originates from the 1980 s. It was developed to mimic ...
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Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggrega...
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Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg
Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and enga...
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ISBN:
(数字)9798350385328
ISBN:
(纸本)9798350385335
Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.
This paper presents an improved compact model for TeraFETs employing a nonlinear transmission line approach to describe the non-uniform carrier density oscillations and electron inertia effects in the TeraFET channels...
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