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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Bracho, Jorge Charalambous, Chrysanthos Efthimiou, Venizelos Lagos, Dimitrios Samotyjak, Tomasz Askeland, Magnus Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus Efficient and Sustainable Energy Solutions PHAETHON Centre of Excellence for Intelligent Nicosia Cyprus Energy Systems Analytics Group Barcelona Spain Instytut Energetyki Gdansk Poland SINTEF Energy Research Energy Systems Trondheim Norway

ISBN: (纸本)9798350375923

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 task. This is particularly the case for systems with different energy sources, as they'd be able to either address one specific type of energy demand and/or allow for the coupling of different energy carriers that would allow for technologies that normally don't interact to start complementing each other. Therefore, this paper proposes various scenarios for the UCY campus in which hydrogen technologies would allow for the electrification of thermal demand while harnessing the surplus production coming from a solar park, which is modelled through the Integrate optimization tool and validated through DidSILENT PowerFactory. The results showcased a rise of up to 58% in self-sufficiency while the annual energy costs and CO2 emissions were reduced by 45% and 48% respectively. © 2024 IEEE.

关键词： Clean energy

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

arXiv

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arXiv 2025年

作者： Kaypak, Ali Umut Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical and Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging 3D convex shape-based High-Order control Barrier Functions (HOCBFs) to address this issue. While prior works focused on using control Barrier Functions (CBFs) for human–robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with any nominal controller and ensure safety without significantly restricting the robots’ workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. © 2025, CC BY.

关键词： Robotic arms

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

Self-supervised Contrastive Learning for 6G UM-MIMO THz Comm...

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IEEE International Conference on Communications Workshops, ICC

作者： Rafid Umayer Murshed Md Saheed Ullah Mohammad Saquib Moe Z. Win Department of Electrical and Computer Engineering The University of Texas Dallas Texas USA Department of Electrical and Computer Engineering University of Delaware Delaware USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts USA

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.

关键词： 6G mobile communication Wireless communication Array signal processing Heuristic algorithms Conferences System performance Contrastive learning

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Distributionally robust model predictive control for wind farms

arXiv

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arXiv 2023年

作者： Mark, Christoph Liu, Steven Institute of Control Systems Department of Electrical and Computer Engineering University of Kaiserslautern-Landau Kaiserslautern67663 Germany

In this paper, we develop a distributionally robust model predictive control framework for the control of wind farms with the goal of power tracking and mechanical stress reduction of the individual wind turbines. We introduce an ARMA model to predict the turbulent wind speed, where we merely assume that the residuals are sub-Gaussian noise with statistics contained in an moment-based ambiguity set. We employ a recently developed distributionally model predictive control scheme to ensure constraint satisfaction and recursive feasibility of the control algorithm. The effectiveness of the approach is demonstrated on a practical example of five wind turbines in a row. © 2023, CC BY-NC-ND.

关键词： Wind turbines

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Improving Load Profile Through Optimal Load Scheduling Using Genetic Algorithms

Improving Load Profile Through Optimal Load Scheduling Using...

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International Conference on Advanced electrical engineering (ICAEE)

作者： Rihab Belhabri Sid-Ahmed Zidi Samir Hadjeri Halima Boubekeur Electrical Engineering Department Intelligent Control & Electrical Power Systems Laboratory (ICEPS) Djillali Liabès University Sidi-bel-abbés Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

This paper presents an innovative methodology based on a Genetic Algorithm (GA) to optimize Demand-Side Management (DSM) in the energy domain. The proposed approach utilizes a GA to analyze appliance consumption profiles and identify load shifting opportunities for controllable appliances from peak hours to off-peak hours, defined by Demand Response (DR) programs such as Time-of-Use (TOU) pricing. This optimization enables the reduction of electricity consumption during peak hours, lowers consumer bills, and improves the efficiency of the power grid, contributing to more sustainable and economical energy management.

关键词： electrical engineering Energy consumption Costs Electricity Pricing Scheduling Power grids Energy efficiency Optimization Genetic algorithms

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Equilibrium mechanism between dc voltage and ac frequency for ac-dc interlinking converters

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iEnergy 2022年第3期1卷 279-284页

作者： Haixu Shi Kai Sun Xiaochao Hou Yunwei Li Haihao Jiang Guangdong Midea Refrigeration Equipment Co. Ltd.GuangdongFoshan 528311China Department of Electrical Engineering State Key Laboratory of Power SystemsTsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 1H9Canada

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.

关键词： Virtual machine interlinking converter unified control microgrid.

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An Optimization-Based HVAC Load and PV Disaggregation Methodology With Contextual Supervision

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IEEE Transactions on Smart Grid 2025年

作者： Kim, Hyeoniin Yu, Min Gyung Wu, Di Lu, Ning Systems Center Electrical and Computer Engineering Department RaleighNC27606 United States Pacific Northwest National Laboratory RichlandWA99354 United States

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 strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV. © 2010-2012 IEEE.

关键词： Load disaggregation optimization-based blind source separation

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Point Density-aware Channel-wise Transformer for LiDAR 3D Object Detection 20

Point Density-aware Channel-wise Transformer for LiDAR 3D Ob...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Fanhang Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Xiqing District Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

We present a two-stage 3D object detection framework from point clouds, named Point Density-aware Channel-wise Transformer (PD-CT3D), which investigate the property of point density. This architecture uses 3D sparse CNN to effectively generate high-quality proposals at the first stage, then integrates the inherent property of point density and 3D CNN-based voxel features by density-aware proposal grid pooling. Specifically, each generated proposal from the first stage is divided into grids to aggregate corresponding voxel-wise features and raw point-based features for encoding the representative features from the whole scene. Subsequently, a channel-wise encoder-decoder transformer is adopted to extract the encoded density-aware features and decode them into a global representation for final refinement. Experiments on the widely used KITTI dataset show that the PD-CT3D achieves competitive performance among state-of-the-art methods. © 2023 IEEE.

关键词： Object detection

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Risk Assessment with Generic Energy Storage under Exogenous and Endogenous Uncertainty

Risk Assessment with Generic Energy Storage under Exogenous ...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Qi, Ning Cheng, Lin Wan, Yuxiang Zhuang, Yingrui Liu, Zeyu State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China Tianjin University China

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Current risk assessment ignores the stochastic nature of energy storage availability itself and thus lead to potential risk during operation. This paper proposes the redefinition of generic energy storage (GES) that is allowed to offer probabilistic reserve. A data-driven unified model with exogenous and endogenous uncertainty (EXU & EDU) description is presented for four typical types of GES. Moreover, risk indices are proposed to assess the impact of overlooking (EXU & EDU) of GES. Comparative results between EXU & EDU are illustrated in distribution system with day-ahead chance-constrained optimization (CCO) and more severe risks are observed for the latter, which indicate that system operator (SO) should adopt novel strategies for EDU uncertainty. © 2022 IEEE.

关键词： Risk assessment

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