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Design Considerations for Electric Traction Power systems

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Design Considerations for Electric Traction Power Systems

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Jigyesh Sharma Kushan Tharuka Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

There is growing interest in e-mobility which is not only limited to electric vehicles but also mass transit systems, heavy duty vehicles, off-road vehicles and so on. With the intense focus on sustainability and achieving net zero emissions, most transit systems will be electrified. The impact of mass transit electrification on the existing power system needs a thorough and detailed analysis. Also, the upgrade from fuel powered transit systems to electric transit systems needs to be done with minimal changes to the existing power network and maximum utilization of available resources in the transit system. Therefore, the study of electric transit systems, its impact on the power system and challenges that can arise from electrifying railways are presented in this paper.

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Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks

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arXiv 2022年

作者： Agnew, Dennis Boamah, Sharon Mathieu, Reynold Cooper, Austin McNair, Janise Bretas, Arturo Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Distributed Systems Group Pacific Northwest National Laboratory RichlandWA United States

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput;a reduction in latency of > 20%;and an increase in throughput of approximately 11% during the DoS attack scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Cybersecurity

Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework

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arXiv 2023年

作者： Zheng, Jingheng Ni, Wanli Tian, Hui Gündüz, Deniz Quek, Tony Q.S. Han, Zhu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Modena41100 Italy The Singapore University of Technology and Design 487372 Singapore The Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

Learning For Predictive Control: A Dual Gaussian Process Approach

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arXiv 2022年

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

Recent Advancements in Vehicle to Vehicle Charging

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Recent Advancements in Vehicle to Vehicle Charging

International Conference on Power Electronics and Drive systems (PEDS)

作者： Anindita Golder Kushan Tharuka Lulbadda Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

More and more electric vehicles and charging stations are penetrating into the power system today which introduce new challenges. As a result, the concept of vehicle to vehicle charging (V2V) is gaining interest. Therefore, it is important to understand the concept of V2V charging. In this paper, some of the recent developments in the area of vehicle to vehicle (V2V) charging are explored.

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Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty

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arXiv 2022年

作者： Yurdakul, Ogun Qiu, Feng Albayrak, Sahin Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Energy Systems Division Argonne National Laboratory LemontIL60439 United States

To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and temporal information, these models are typically plagued by a poor out-of-sample performance. To effectively exploit contextual information, in this paper, we formulate a conditional SUC problem that is solved given a covariate observation. The presented problem relies on the true conditional distribution of net load and so cannot be solved in practice. To approximate its solution, we put forward a predictive prescription framework, which leverages a machine learning model to derive weights that are used in solving a reweighted sample average approximation problem. In contrast with existing predictive prescription frameworks, we manipulate the weights that the learning model delivers based on the specific dataset, present a method to select pertinent covariates, and tune the hyperparameters of the framework based on the out-of-sample cost of its policies. We conduct extensive numerical studies, which lay out the relative merits of the framework vis-à-vis various benchmarks. © 2022, CC BY.

关键词： Stochastic models

Sliding Mode Control for a DC-DC Buck Converter Feeding a Constant Power Load: A Practical Implementation

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Sliding Mode Control for a DC-DC Buck Converter Feeding a Co...

Advanced Technology in Electronic and electrical engineering (ICATEEE), International Conference of

作者： Khalil Louassaa Aissa Chouder Mahdi Boukerdja Abdelhafid Cherifi Ali Aillane Electrical Engineering Department Electrical Engineering Laboratory University of M’sila M’sila Algeria Materials and Electronic Systems Laboratory University of Bordj Bou Arreridj Bordj Bou Arreridj Algeria Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (LabSTA) National School of Engineering of Sfax (ENIS) University of Sfax Sfax Tunisia

DC microgrids have gained in popularity in recent years due to features such as greater reliability, high efficiency, and control simplicity. Constant power loads (CPLs), on the other hand, are a problem and an effective cause of instability in DC MGs. To address the aforementioned issue, this paper discusses the stabilization of a buck power converter that supplies CPL via a regulated boost power converter. A robust sliding mode control is proposed, which stabilizes the output voltage and ensures the CPL's required power. The system is investigated in the context of external perturbations such as fluctuating input voltage and sudden change in the CPL. The robustness and efficacy of the proposed method have been demonstrated to confirm the system's overall stability. Simulation and experimentation are employed to verify the provided controller. The results verify the suggested controller's superiority.

关键词： Damping Voltage measurement Perturbation methods Microgrids Stability analysis Robustness Steady-state

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Triple Point Masking

arXiv 2024年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. Our code and models are available at https://***/liujia99/TPM. © 2024, CC BY.

关键词： Signal encoding

Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs

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Deep Neural Network Architecture Search for Accurate Visual ...

IEEE International Conference on Robotics and Automation (ICRA)

作者： E. Cereda L. Crupi M. Risso A. Burrello L. Benini A. Giusti D. Jahier Pagliari D. Palossi Dalle Molle Institute for Artificial Intelligence USI and SUPSI Lugano Switzerland Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Integrated Systems Laboratory ETH Zürich Zürich Switzerland

Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human surroundings. The simple electronics aboard such robots (sub-100 mW) make them particularly cheap and attractive but pose significant challenges in enabling onboard sophisticated intelligence. In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task. Our work demonstrates how reallife and field-tested robotics applications can concretely leverage NAS technologies to automatically and efficiently optimize CNNs for the specific hardware constraints of small UAVs. We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and ~50Hz@90mW.

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