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Artificial intelligence-based system frequency response modeling considering contribution of inverter-based resources

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Neural Computing and Applications 2024年 1-12页

作者： Feizi, Amir Golpîra, Hêmin Power Systems Modeling & Simulation Lab. Department of Electrical Engineering University of Kurdistan Kurdistan Sanandaj Iran

This paper proposes an artificial intelligence-based approach to address the contribution of inverter-based resources (IBRs) in the system frequency response (SFR) model. IBRs are assumed to be aggregated within a dynamic virtual power plant (DVPP), a new entity that can be summoned by the system operator to provide ancillary services. Current SFR models in the presence of IBRs are computationally expensive and neglect the synchronization dynamics between conventional synchronous generators (SGs) and IBRs. In conventional SFR models, the interactions between SGs were formulated through a unique synchronization coefficient. However, in modern power systems, the interaction between SGs and IBRs within DVPPs should be represented by a 2 × 2 synchronization coefficients (SCs) matrix. Hence, an attempt is made to deal with the calculation of the SCs matrix using optimization algorithms and a neural network. More precisely, optimization algorithms are employed to calculate the SCs at different prespecified operating points, which are then fed into a neural network for training purposes, enabling online SFR derivation. The proposed approach is characterized by its low computational burden and complexity, which are critical for accommodating different levels of DVPP penetration. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Virtual Power Plants

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An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 707-718页

作者： Sirwan Shazdeh Hêmin Golpîra Hassan Bevrani the SMGRC University of KurdistanSanandajIran Power Systems Modeling&Simulation Lab Department of Electrical EngineeringUniversity of KurdistanKurdistanSanandaj 66177-15175Iran

This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units(PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.

关键词： Fuzzy logic fault-induced delayed voltage recovery phasor measurement unit(PMU) short-term voltage stability situational awareness voltage classification

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A Comparative Design Study of Topologically Dissimilar Automotive Structures

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Computer-Aided Design and Applications 2024年第3期21卷 374-391页

作者： Ramnath, Satchit Shah, Jami J. Detwiler, Duane Simulation Innovation and Modeling Center The Ohio State University United States Department of Mechanical Engineering The Ohio State University United States Honda Development and Manufacturing of Americas LLC United States

Automotive body structure design is critical to meet various design requirements primarily based on the engineer’s experience. One of the critical automotive body components is the frame that supports the hood, which withstands many static, dynamic, and local impact loads. In the current design process, designers improve upon previous-generation designs to meet updated targets. However, this process diminishes the possibility of adapting design ideas from other models. Consequently, there is a trend to start with topology optimization and then optimize for size. However, a smooth transition between meshes produced by topology optimization and parametric CAD does not exist. The uniqueness of each design and nonuniform parameters make it difficult to compare multiple designs and extract useful feature information. The proposed methods use the existing dataset of hood frames with parameterized CAD geometries. It is aimed at guiding designers for a new hood frame. Hood frames vary in shape (ribs, pocket features, and layout) and size (dimensions of shapes and features). Conventional designs of experiments cannot be used since the designs contain different geometric features, layouts, and parameterization schemes. The research proposes a non-uniform parametric study using a pseudo-design of experiments to provide guidance. It uses an experiment design for parameter reduction, and parameter correlation and then runs finite element analysis (FEA) for a given set of loads. The response data generated from this FEA is processed and analyzed using multiple pseudo-experiment design methods to make predictions on the ideal features to be used in a design. Each method can be used separately to perform comparative studies in understanding the effect of features in dissimilar structures. © 2024 U-turn Press LLC.

关键词： Structural optimization

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Developing a 3D Laparoscopy Training Application to Assess the Efficacy in Virtual Reality Environments 9th

Developing a 3D Laparoscopy Training Application to Assess t...

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9th EAI International Conference on Smart Objects and Technologies for Social Goods, GOODTECHS 2023

作者： Yosunkaya, Ege Şahin, Sebahat Selin Surer, Elif Keleş, Hasan Onur Department of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara06800 Turkey Department of Biomedical Engineering Ankara University Ankara06830 Turkey

ISBN: (纸本)9783031525230

This study aims to develop a multimodal understanding of transferring an established method of laparoscopy training to the virtual reality domain. The virtual reality version of the laparoscopic box trainer is developed and tested with 15 participants. Post-experiment questionnaires showed the version of the simulation with tutorial and haptic feedback is acceptable in terms of usability and received better feedback in the technology acceptance model questionnaire. Furthermore, the kinematic behavior of participants’ hands showed a significant distinction between above-average and below-average completion time groups similar to the physical and computer-based non-immersive simulation counterparts. The physiological response of the participants is investigated between rest state and during the task with an Electrocardiogram (ECG) and indicators of increased mental workload are observed with increased heart rate and decreased heart rate variability. The interest in assessing the physiological and kinematic features of trainees in a virtual reality (VR) environment is on the rise and the proposed study is very promising in terms of enhancing the development of improved training and assessment methodologies. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Virtual reality

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Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo

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npj Computational Materials 2024年第1期10卷 2360-2369页

作者： Stephan Thaler Felix Mayr Siby Thomas Alessio Gagliardi Julija Zavadlav Professorship of Multiscale Modeling of Fluid Materials Department of Engineering Physics and ComputationTUM School of Engineering and DesignTechnical University of MunichMunichGermany Valence Labs MontrealQCCanada Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical EngineeringTUM School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Munich Data Science Institute Technical University of MunichMunichGermany

Metal-organic frameworks(MOF)are an attractive class of porous materials due to their immense design space,allowing for application-tailored *** of interest,such as gas sorption,can be predicted in silico with molecular mechanics ***,the accuracy is limited by the available empirical force field and partial charge estimation *** this work,we train a graph neural network for partial charge prediction via active learning based on Dropout Monte *** show that active learning significantly reduces the required amount of labeled MOFs to reach a target *** obtained model generalizes well to different distributions of MOFs and *** addition,the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen *** work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges,supporting insilico material design.

关键词： properties charge estimation

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Transmission Power Control for Interference Reduction in Cellular D2D Networks

Transmission Power Control for Interference Reduction in Cel...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Sadehvand, Mehrdad Moghim, Neda Ghahfarokhi, Behrouz Shahgholi Shetty, Sachin University of Isfahan Department of Computer Engineering Isfahan Iran Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk United States

ISBN: (纸本)9798350335590

Interference is one of the most critical issues in the cellular Device-to-Device (D2D) networks. The sharing of radio resources in cellular and D2D communications offers spectrum efficiency advantages for the cellular network. However, resource sharing also introduces interference, leading to a reduction in the quality of service experienced by users. In this paper, we propose an adversarial Multi-Armed Bandit learning-based transmission Power Control method called MAB-PC for both cellular and D2D transmitters. The objective of this method is to ensure the minimum service quality for users, considering the minimum spectral efficiency and maximum block error rate. Additionally, to address the conflicting objectives of D2D and cellular communications, MAB-PC is modeled as a Pareto optimization problem for D2D transmitters. MAB-PC is a distributed method that minimizes signaling overhead and relies solely on local Channel State Information (CSI). The effectiveness of the proposed method is evaluated based on reliability, total data rate, spectral efficiency, block error rate, and outage probability, demonstrating superior performance compared to its counterpart. © 2023 IEEE.

关键词： Spectrum efficiency

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Multimodal Data-fusion in Fluorescence Lifetime Imaging using Deep Learning

Multimodal Data-fusion in Fluorescence Lifetime Imaging usin...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Pandey, Vikas Erbas, Ismail Intes, Xavier Center for Modeling Simulation and Imaging in Medicine Rensselaer Polytechnic Institute TroyNY United States Biomedical Engineering Department Rensselaer Polytechnic Institute TroyNY United States

We report a novel deep learning (DL)-based multi-modal data-fusion approach in generating high-resolution time-resolved images in fluorescence lifetime imaging (FLI). The performance of this DL model has been validate... 详细信息

ISBN: (纸本)9781957171340

关键词： Data fusion

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NUMERICAL FLOW VISUALIZATION: VISTA AND EXPEDITION

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Journal of Flow Visualization and Image Processing 2022年第3期29卷 1-27页

作者： Liu, Zhanping Department of Computational Modeling and Simulation Engineering Old Dominion University ODU-CMSE 1300 ECSB 4700 Elkhorn Avenue NorfolkVA23529 United States

Numerical flow visualization resorts to data processing, feature extraction, geometric construc-tion, image synthesis, graphical display, and scene manipulation to enable intuitive insightful interactive analysis of tangential directions of movement embedded in a velocity vector field. This paper presents some advances in numerical flow visualization over the past three decades, with emphasis on geometry-based and texture-based methods. Also introduced in this context is our own research for representing, depicting, and exploring flow data. Not intended for an exhaustive survey of the literature, our discussions revolve around streamline integration, interactive streamline placement, automatic streamline placement (including evenly spaced layout of streamlines), streamline clustering, time-dependent flow lines (e.g., pathlines), flow texture synthesis (e.g., line integral convolution), feature mining (e.g., flow topology), and parallel vi-sualization. These topics address flows ranging from planar to surface and volumetric domains, from steady to unsteady cases, and from Cartesian to curvilinear and unstructured grids. This scope allows us to examine a variety of approaches in the working mechanism, numerical accu-racy, representation effectiveness, visual quality, and computational performance so as to show the respective advantages and disadvantages. The findings may help visualization researchers pinpoint main long-lasting challenges, visualization developers identify practically applicable techniques, and domain scientists perceive important promising advances in numerical flow visualization. © 2022 by Begell House, Inc.

关键词： Textures

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Data-Driven Modelling of Electrode Resistance in Submerged Arc Furnaces

Data-Driven Modelling of Electrode Resistance in Submerged A...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Sinchan Biswas Vetle Kjær Risinggård Manuel Sparta Damiano Varagnolo Department of Engineering Cybernetics NTNU Trondheim Norway Modeling and Simulation group NORCE Kristiansand Norway

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

We consider the problem of finding control-oriented models for the electrode resistance of submerged arc furnaces (SAF) to aid the metallurgical process control of ferrosilicon production. To accomplish this goal, we analyze the field data gathered from the Norwegian metal producer Wacker AS, which are the most important input parameters for accurately predicting electrode resistances. This is done by investigating the predictive capabilities of different linear and non-linear model structures in different furnace operating conditions, and discussing which type of non-linearity induces the best-performing models both in terms of prediction fit and modeling error in opportune test sets. We finally provide interpretations of why the presence of this non-linearity results in the best performance by connecting their structure with domain expertise about the electrical dynamics within SAF circuits.

关键词： Electrodes Resistance Industrial electronics Furnaces Circuits Process control Metals

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INVESTIGATING THE IMPACT OF PANDEMIC ON THE PERIOPERATIVE HEALTHCARE WORKERS AVAILABILITY: AN AGENT-BASED APPROACH

INVESTIGATING THE IMPACT OF PANDEMIC ON THE PERIOPERATIVE HE...

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2024 Winter simulation Conference, WSC 2024

作者： Prasad, Shweta Prabhu, Vishnunarayan Girishan Hand, William Industrial and Systems Engineering Department University of North Carolina CharlotteNC United States School of Modeling Simulation Training University of Central Florida OrlandoFL United States Department of Anesthesiology PRISMA Health - Upstate GreenvilleSC United States

ISBN: (纸本)9798331534202

Protecting healthcare workers (HCWs) during a pandemic is critical to provide timely medical care for patients. Although prior studies have investigated HCW unavailability during the COVID-19 pandemic, the studies have not investigated the impact of parameters such as patient census, vaccination rates, transmission rates, and multiple hospital locations on HCW availability. This research considers a high-risk HCW group of perioperative staff to investigate the impact of segregating and rotating staffing policies on HCW unavailability during a pandemic in a health system with multiple locations. An agent-based model with a SEIR compartmental model was developed to simulate various scenarios. Simulated findings indicate that segregating and rotating policies significantly (p-value © 2024 IEEE.

关键词： COVID-19

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