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arXiv 2020年

作者： Chen, Kaixuan Lin, Jin Qiu, Yiwei Liu, Feng Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Electrical and Computer Engineering University of Macau 999078 China

To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines, which remarkably influence the WF dynamic power production. The nonlinear and high-dimensional nature of dynamic wake model, however, brings extremely high computation complexity and obscure the design of WF controllers. This paper overcomes the control difficulty brought by the dynamic wake model by proposing a novel control-oriented reduced order WF model and a deep-learning-aided model predictive control (MPC) method. Leveraging recent advances in computational fluid dynamics (CFD) to provide high-fidelity data that simulates WF dynamic wake flows, two novel deep neural network (DNN) architectures are specially designed to learn a dynamic WF reduced-order model (ROM) that can capture the dominant flow dynamics. Then, a novel MPC framework is constructed that explicitly incorporates the obtained WF ROM to coordinate different turbines while considering dynamic wake interactions. The proposed WF ROM and the control method are evaluated in a widely-accepted high-dimensional dynamic WF simulator whose accuracy has been validated by realistic measurement data. A 9-turbine WF case and a larger 25-turbine WF case are studied. By reducing WF model states by many orders of magnitude, the computational burden of the control method is reduced greatly. Besides, through the proposed method, the range of AGC signals that can be tracked by the WF in the dynamic operation is extended compared with the existing greedy controller. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

arXiv

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arXiv 2024年

作者： Lian, Haojie Li, Xinhao Qu, Yilin Du, Jing Meng, Zhuxuan Liu, Jie Chen, Leilei Key Laboratory of In-situ Property-improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan100190 China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architecture and Civil Engineering Huanghuai University Zhumadian463000 China School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Northwestern Polytechnical University Xi’an710072 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China Computer Engineering Department Taiyuan Institute of Technology Taiyuan030008 China

Neural radiance fields (NeRFs) are a deep learning technique that can generate novel views of 3D scenes using sparse 2D images from different viewing directions and camera poses. As an extension of conventional NeRFs in underwater environment, where light can get absorbed and scattered by water, SeaThru-NeRF was proposed to separate the clean appearance and geometric structure of underwater scene from the effects of the scattering medium. Since the quality of the appearance and structure of underwater scenes is crucial for downstream tasks such as underwater infrastructure inspection, the reliability of the 3D reconstruction model should be considered and evaluated. Nonetheless, owing to the lack of ability to quantify uncertainty in 3D reconstruction of underwater scenes under natural ambient illumination, the practical deployment of NeRFs in unmanned autonomous underwater navigation is limited. To address this issue, we introduce a spatial perturbation field Dω based on Bayes’ rays in SeaThru-NeRF and perform Laplace approximation to obtain a Gaussian distribution N(0, Σ) of the parameters ω, where the diagonal elements of Σ correspond to the uncertainty at each spatial location. We also employ a simple thresholding method to remove artifacts from the rendered results of underwater scenes. Numerical experiments are provided to demonstrate the effectiveness of this approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Distributed Coordinated Emergency Frequency Control Strategy for Renewables-Rich Power Systems Based on Multi-Agent Reinforcement Learning

SSRN

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SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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The Future of Fighter Pilot Training? Live Virtual Constructive in Large Force Exercises: Perceived and Expected Training Value

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The International Journal of Aviation Psychology 2024年第1期34卷

作者： Rogier Woltjer Robert Ramberg Henrik Artman Sanna Aronsson Mikael Mitchell Per-Anders Oskarsson a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenb Department of Computer and Systems Sciences Faculty of Social Sciences Stockholm University Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenc Division of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedend 221 Sqn Swedish Air Force Luleå Sweden e Department of Man-Technology-Organisation Swedish Defence Research Agency FOI Linköping Sweden

ABSTRACTObjectiveThis study investigates whether and how perceived training value varies among fighter pilots participating in a large force exercise (LFE), and whether they expect Live Virtual Constructive (LVC) training to enhance training value of future *** technology is maturing but its expected training value is under-investigated, especially regarding future LVC in *** pilots at a Live-flying LFE answered questionnaires, 120 responses were analyzed statistically. The questions focused on whether the sortie they just flew provided training value, generally and for specific Desired Learning Objectives (DLOs), on whether the inclusion of Virtual and Constructive entities in future LFEs would contribute to training value generally and to the DLOs specifically, and on specific LVC implementation *** of experienced training value varied across DLOs, and DLO ratings varied across Mission types. Fighter pilots were positive toward including Virtual and Constructive entities in LFEs for certain DLOs, mostly for flying a complex mission. DLOs covaried around the factors of LVC-tradeoff advantages and Live advantages, as well as temporal dimensions of planning before a mission and stages of engagement. LVC implementation lessons were *** pilots are generally moderately positive about LVC in LFEs. The measurement of perceived training value per DLO seems a relevant instrument for investigating perceived training value of an LFE and expected training value of future LVC training.

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Iterative rational krylov algorithms for model reduction of a class of constrained structural dynamic system with engineering applications

arXiv

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arXiv 2021年

作者： Du, Xin Uddin, Mohammad Monir Fony, A. Mostakim Hossain, Md. Tanzim Shuzan, Md. Nazmul Islam School of Mechatronic Engineering and Automation Shanghai University Shanghai200072 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Mathematics and Physics North south University Dhaka1229 Bangladesh Department of Mathematics Chittagong University Chittagong Bangladesh Department of Electrical and Computer Engineering North South University Dhaka1229 Bangladesh

This paper discusses model order reduction of large sparse second-order index-3 differential algebraic equations (DAEs) by applying Iterative Rational Krylov Algorithm (IRKA). In general, such DAEs arise in constraint mechanics, multibody dynamics, mechatronics and many other branches of sciences and technologies. By deflecting the algebraic equations the second-order index-3 system can be altered into an equivalent standard second-order system. This can be done by projecting the system onto the null space of the constraint matrix. However, creating the projector is computationally expensive and it yields huge bottleneck during the implementation. This paper shows how to find a reduce order model without projecting the system onto the null space of the constraint matrix explicitly. To show the efficiency of the theoretical works we apply them to several data of second-order index-3 models and experimental resultants are discussed in the paper. © 2021, CC BY.

关键词： Differential equations

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Numerical Analysis of Electrical Discharge in DC Switches under External Circuit Parameter Variations 5

Numerical Analysis of Electrical Discharge in DC Switches un...

引用

5th International Conference on Electric Power Equipment - Switching Technology, ICEPE-ST 2019

作者： Seo, Kyung Sik Baek, Myung Ki Park, Il Han Sungkyunkwan University School of Electronic and Electrical Engineering Suwon Korea Republic of Korea Electrotechnology Research Institute Computer Simulation-Based Design Department Changwon Korea Republic of

ISBN: (纸本)9781728152189

This paper presents a self-consistent and multiphysics discharge model to analyze the characteristics of the electric discharge generated in a current-switching system. Switching discharge is numerically analyzed by a simplified one-dimensional model using the finite element method. Several simulations with different external resistances were performed to analyze the effect of the external circuit parameter on electric discharge. © 2019 IEEE.

关键词： Electric discharges

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Nonintrusive uncertainty quantification of dynamic power systems subject to stochastic excitations

arXiv

引用

arXiv 2020年

作者： Qiu, Yiwei Lin, Jin Chen, Xiaoshuang Liu, Feng Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Electrical and Computer Engineering University of Macau 999078 China Department of Electrical Engineering Tsinghua University Beijing100087 China

Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an increasing impact on power system dynamics;However, except from the Monte Carlo simulation, most existing methods for quantifying this impact are intrusive, meaning they are not based on commercial simulation software and hence are difficult to use for power utility companies. To fill this gap, this paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. First, the Gaussian or non-Gaussian stochastic excitations are modeled with an Itô process as stochastic differential equations. Then, the Itô process is spectrally represented by independent Gaussian random parameters, which enables the polynomial chaos expansion (PCE) of the system dynamic response to be calculated via an adaptive sparse probabilistic collocation method. Finally, the probability distribution and the high-order moments of the system dynamic response and performance index are accurately and efficiently quantified. The proposed nonintrusive method is based on commercial simulation software such as PSS/E with carefully designed input signals, which ensures ease of use for power utility companies. The proposed method is validated via case studies of IEEE 39-bus and 118-bus test *** codes 93E10 Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Direct Optical Probing of the Magnetic Properties of the Layered Antiferromagnet CrPS4

arXiv

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arXiv 2024年

作者： Fąs, Tomasz Wlazlo, Mateusz Birowska, Magdalena Rybak, Milosz Zinkiewicz, Malgorzata Oleschko, Leon Goryca, Mateusz Gondek, Lukasz Camargo, Bruno Szczytko, Jacek Budniak, Adam K. Amouyal, Yaron Lifshitz, Efrat Suffczyński, Jan Institute of Experimental Physics Faculty of Physics University of Warsaw Pasteura St. 5 Warsaw02-093 Poland Chemical and Biological Systems Simulation Lab Centre of New Technologies University of Warsaw Warsaw02-097 Poland Institute of Theoretical Physics Faculty of Physics University of Warsaw Pasteura St. 5 Warsaw02-093 Poland Department of Semiconductor Materials Engineering Faculty of Fundamental Problems of Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Physics University of Konstanz Universitätsstraße 10 KonstanzD-78457 Germany Faculty of Physics and Applied Computer Science AGH University of Kraków Mickiewicza 30 Kraków30-059 Poland Schulich Faculty of Chemistry Technion – Israel Institute of Technology Haifa3200003 Israel Department of Materials Science and Engineering Technion – Israel Institute of Technology Haifa3200003 Israel

Unusual magnetic properties of Van der Waals type antiferromagnetic semiconductors make them highly attractive for spintronics and optoelectronics. A link between the magnetic and optical properties of those materials, required for practical applications, has not been, however, established so far. Here, we report on a combined experimental and theoretical study of magnetic, optical, and structural properties of bulk CrPS4 samples. We find that the magnetic-field-dependent circular polarization degree of the photoluminescence is a direct measure of the net magnetization of CrPS4. Complementary, Raman scattering measured as a function of magnetic field and temperature enables the determination of the magnetic susceptibility curve of the material. Our experimental results are backed by Our experimental results are supported by density functional theory calculations that take as input the lattice parameters determined from temperature-dependent X-ray diffraction measurements. This allows us to explain the impact of spin ordering on the spectral position of Raman transitions in CrPS4, as well as anomalous temperature shifts of selected of them. The presented method for all-optical determination of the magnetic properties is highly promising for studies of spin ordering and magnetic phase transitions in single- or a few-layer samples of magnetic layered materials, for which a poor signal-to-noise ratio precludes any reliable neutron scattering or magnetometry measurements. © 2024, CC BY.

关键词： Magnetic susceptibility

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Differentially Private Top-k Items Based on Least Mean Square——Take E-Commerce Platforms for Example

引用

Wuhan University Journal of Natural Sciences 2019年第2期24卷 98-106页

作者： CAO Mengyue WU Fusheng NI Mingtao XIONG Xingxing LIU Shubo WANG Jun Department of Foreign Languages Yangtze University College of Arts and Sciences Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance and Economics School of Cyber Science and Engineering Wuhan University School of Computer Science Leshan Normal University School of Computer Wuhan University College of Computer South-Central University for Nationalities

User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing,which may bring users the risk of privacy *** this paper,we explore the problem of differential private top-k items based on least mean ***,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on *** show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server *** evaluate our algorithm with three real datasets,and the experimental results show that the precision of our method reaches 85%with strong privacy protection,which outperforms the Kalman filter-based existing methods.

关键词： top-k differential privacy least mean square streaming data

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Federated simulation for medical imaging 23rd

Federated simulation for medical imaging

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Daiqing Kar, Amlan Ravikumar, Nishant Frangi, Alejandro F. Fidler, Sanja NVIDIA Toronto Canada Vector Institute Toronto Canada Department of Computer Science University of Toronto Toronto Canada CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom LICAMM Leeds Institute of Cardiovascular and Metabolic Medicine School of Medicine University of Leeds Leeds United Kingdom Department of Cardiovascular Sciences and Department of Electrical Engineering ESAT/PSI KU Leuven Leuven Belgium

ISBN: (纸本)9783030597092

Labelling data is expensive and time consuming especially for domains such as medical imaging that contain volumetric imaging data and require expert knowledge. Exploiting a larger pool of labeled data available across multiple centers, such as in federated learning, has also seen limited success since current deep learning approaches do not generalize well to images acquired with scanners from different manufacturers. We aim to address these problems in a common, learning-based image simulation framework which we refer to as Federated simulation. We introduce a physics-driven generative approach that consists of two learnable neural modules: 1) a module that synthesizes 3D cardiac shapes along with their materials, and 2) a CT simulator that renders these into realistic 3D CT Volumes, with annotations. Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers. We show that our data synthesis framework improves the downstream segmentation performance on several datasets. © Springer Nature Switzerland AG 2020.

关键词： Medical imaging

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