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arXiv 2023年

作者： Weber, Moritz Janecke, Luc Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

In the present paper, we introduce a new method for the automated generation of residential distribution grid models based on novel building load estimation methods and a two-stage optimization for the generation of the 20 kV and 400 V grid topologies. Using the introduced load estimation methods, various open or proprietary data sources can be utilized to estimate the load of residential buildings. These data sources include available building footprints from OpenStreetMap, 3D building data from OSM Buildings, and the number of electricity meters per address provided by the respective distribution system operator (DSO). For the evaluation of the introduced methods, we compare the resulting grid models by utilizing different available data sources for a specific suburban residential area and the real grid topology provided by the DSO. This evaluation yields two key findings: First, the automated 20 kV network generation methodology works well when compared to the real network. Second, the utilization of public 3D building data for load estimation significantly increases the resulting model accuracy compared to 2D data and enables results similar to models based on DSO-supplied meter data. This substantially reduces the dependence on such normally proprietary data. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization 63

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： You, Jiawei Dai, Xinliang Jiang, Yuning Yin, Haoyu Shi, Yuanming Jones, Colin N. ShanghaiTech University School of Information Science and Technology China Epfl Switzerland Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Epfl Automatic Control Laboratory Switzerland Washington University in St. Louis Department of Electrical and Systems Engineering United States

ISBN: (纸本)9798350316339

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Predictive control systems

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Improving Anomaly Detection with Adaptive Dynamic Threshold: A Review and Enhanced Method

Improving Anomaly Detection with Adaptive Dynamic Threshold:...

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International Conference on System Reliability and Science (ICSRS)

作者： Arman Aghaei Attar Kaibin Bao Veit Hagenmeyer Tagir Fabarisov Andrey Morozov Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

In fast-growing modern cyber-physical systems, reliability plays a vital role. Effective anomaly detection, which identifies security and safety issues at early stage, is essential for ensuring system reliability. While much research has focused on anomaly detection techniques, fewer studies address a key challenge, setting precise and responsive thresholds for anomaly detection. In this study, we conduct a comprehensive review of current threshold setting methods. Thereafter, we introduce a novel approach for adaptive threshold setting. Our method is tailored for diverse safety and security tasks and is tested on a safety-critical exoskeleton model and a cybersecurity scenario for energy systems. The results demonstrate that our approach could enhance the threshold setting for anomaly detection in CPS.

关键词： Adaptation models Reviews Exoskeletons Time series analysis Cyber-physical systems Network security Safety Distributed power generation Reliability Anomaly detection

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Lowering barriers to a large-scale implementation of grid booster projects by introducing a hybrid concept

Lowering barriers to a large-scale implementation of grid bo...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Jonas Lotze Marvin Dorn Veit Hagenmeyer Strategic Asset Management TransnetBW GmbH Stuttgart Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The future of power systems belongs to renewables. And to integrate them, the expansion of the electricity grids is undoubtedly necessary. Furthermore, power systems cannot operate without reserves and flexibilities. For these to be efficient, system-serving, and economical, holistic concepts are needed. This paper describes the grid booster concept and presents the major barriers to a large-scale implementation in electric power systems. A proposal for enhancing economic viability and multi-applicability is presented through the discussion of a hybrid grid booster concept.

关键词： Hands Renewable energy sources Costs Wind energy Electricity Systems operation Europe Batteries Smart grids Turbines

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Additive Manufacturing: Comparative Study of an IoT Integrated Approach and a Conventional Solution

Additive Manufacturing: Comparative Study of an IoT Integrat...

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Future Technologies Conference, FTC 2020

作者： Shandilya, Harshit Kuchta, Matthias Elkaseer, Ahmed Müller, Tobias Scholz, Steffen G. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9783030630881

The domain of additive manufacturing is an ever-growing sphere of research that will soon make its mark in countless industrial applications. The prowess of additive manufacturing increases significantly when it is integrated along with the Internet of Things. Upon connecting a Prusa MK3S 3D printer to a Raspberry Pi 3B and operating it via Octoprint, the merits of IoT integration are assessed. Comparing the samples from an IoT integrated printer and a standard printer, a contrast is made between the print qualities of the test samples from the two methods of printing. Combining the additive manufacturing set up along with IoT, multiple additional functionalities are obtained, including remote monitoring, remote operation and lower computing requirements. The results of the experiments lead us to the conclusion that the setup can be operated with lesser computing power and also incorporate remote operation and monitoring while maintaining acceptable print quality. The loss in print quality from the IoT approach is negligible when juxtaposed with the prints from a standard desktop operated printer. © 2021, Springer Nature Switzerland AG.

关键词： 3D printers

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CONSTRUCTION AND CONTROL OF VALIDATED HIGHLY CONFIGURABLE MULTI-PHYSICS BUILDING MODELS FOR MULTI-ENERGY SYSTEM ANALYSIS IN A CO-SIMULATION SETUP

arXiv

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arXiv 2025年

作者： Cheng, Haozhen Stock, Jan Xhonneux, André Çakmak, Hüseyin K. Hagenmeyer, Veit Forschungszentrum Jülich Jülich52428 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

Improving energy efficiency by monitoring system behavior and predicting future energy scenarios in light of increased penetration of renewable energy sources are becoming increasingly important, especially for energy systems that distribute and provide heat. On this background, digital twins of cities become paramount in advancing urban energy system planning and infrastructure management. The use of recorded energy data from sensors in district digital twins in collaborative co-simulation platforms is a promising way to analyze detailed system behavior and estimate future scenarios. However, the development and coupling of multi-physics energy system models need to be validated before they can be used for further in-depth analyses. In the present paper, a new multi-physics/-modal and highly configurable building model is presented. Its accuracy and reliability are validated by comparison with data from the TABULA project, ensuring its relevance and applicability to real-world scenarios. The modularity and flexibility with regard to the system configurability of the developed building model is evaluated on various real building types. In addition, the applicability of the building model in a multi-energy system is highlighted by implementing the model in a collaborative co-simulation setup and by coupling it to a district heating grid model in yearly co-simulations. The simulation results for the proposed multi-physical/-modal building modeling concept show a very high level of agreement compared to published reference building data and can therefore be used individually as flexible and modular building models including both thermal and electrical systems for future sector-coupled energy system analyses in view of sustainability. Copyright © 2025, The Authors. All rights reserved.

关键词： District heating

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Optimum settings for PID control of nonlinear systems

arXiv

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arXiv 2025年

作者： Vrabel, R. Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Bottova 25 Trnava917 01 Slovakia

This paper investigates the application of piecewise linear approximation for the control of nonlinear systems, particularly focusing on the effective linearization of systems modeled by the differential equation y(n) + f(y, y', . . . , y(n-1)) = u(t). We explore the use of PID controllers in conjunction with piecewise linearization, tackling the challenges of nonlinear dynamics by dividing the function f into smaller regions or simplices over a predefined compact set D ⊂ Rn. The parameter h determines the number of linear pieces and influences the approximation accuracy. By increasing h, the system's behavior increasingly mirrors that of the original nonlinear system, leading to enhanced performance of the control system. The paper further discusses the optimization of PID controller parameters through the Particle Swarm Optimization method, with a focus on minimizing the ITAE and ISO criteria. Numerical simulations demonstrate the efficacy of the approach, highlighting the trade-off between computational complexity and approximation accuracy in the context of control system *** Codes 93C10 © 2025, CC BY.

关键词： Three term control systems

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Fault Detection in 3D-Printing with Deep Learning

Fault Detection in 3D-Printing with Deep Learning

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Luca Rettenberger Nils Beyer Ingo Sieber Markus Reischl Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen

Fused filament fabrication is a popular 3D printing process where layers of fusible plastic filament are used to build a workpiece. However, the high manual maintenance needs limit its potential. Given the common incidence of defects in the printed components, automated solutions detecting these errors while printing are of great benefit to every practitioner. Current research primarily focuses on monitoring and optimizing manufacturing processes and printing parameters or using additional sensors to directly observe the printing process and identify errors. However, there is no established procedure for assessing print quality in real time with minimal additional sensors. Our proposed computer vision-based deep-learning system overcomes previous limitations by detecting errors in 3D printing and assessing the quality of printed parts in real time using only a single camera. We record and provide a multi-class image dataset that encompasses different printed geometries, error classes, and printing condition variations. Our extensive evaluation shows that considering various geometries and printing conditions is vital for detecting printing errors. Our proposed computer vision-based deep-learning system enhances 3D printing by automating error detection, enabling practitioners to increase efficiency and print high-quality workpieces.

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AutoPQ: AUTOMATING QUANTILE ESTIMATION FROM POINT FORECASTS IN THE CONTEXT OF SUSTAINABILITY

arXiv

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arXiv 2024年

作者： Meisenbacher, Stefan Phipps, Kaleb Taubert, Oskar Weiel, Marie Götz, Markus Mikut, Ralf Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecasting models involves three key challenges: accurate and unbiased uncertainty quantification, workload reduction for data scientists during the design process, and limitation of the environmental impact of model training. In order to address these challenges, we introduce AutoPQ, a novel method designed to automate and optimize probabilistic forecasting for smart grid applications. AutoPQ enhances forecast uncertainty quantification by generating quantile forecasts from an existing point forecast by using a conditional Invertible Neural Network (cINN). AutoPQ also automates the selection of the underlying point forecasting method and the optimization of hyperparameters, ensuring that the best model and configuration is chosen for each application. For flexible adaptation to various performance needs and available computing power, AutoPQ comes with a default and an advanced configuration, making it suitable for a wide range of smart grid applications. Additionally, AutoPQ provides transparency regarding the electricity consumption required for performance improvements. We show that AutoPQ outperforms state-of-the-art probabilistic forecasting methods while effectively limiting computational effort and hence environmental impact. Additionally and in the context of sustainability, we quantify the electricity consumption required for performance improvements. © 2024, CC BY.

关键词： Sustainable development

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Security Fence Inspection at Airports Using Object Detection

Security Fence Inspection at Airports Using Object Detection

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nils Friederich Andreas Specker Jürgen Beyerer Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Fraunhofer IOSB Fraunhofer Center for Machine Learning Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics

To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this pur-pose, security fences are commonly used, but they require regular inspection to detect damages. However, due to the growing shortage of human specialists and the large man-ual effort, there is the need for automated methods. The aim is to automatically inspect the fence for damage with the help of an autonomous robot. In this work, we explore object detection methods to address the fence inspection task and localize various types of damages. In addition to evaluating four State-of-the-Art (SOTA) object detection models, we analyze the impact of several design criteria, aiming at adapting to the task-specific challenges. This in-cludes contrast adjustment, optimization of hyperparameters, and utilization of modern backbones. The experimental results indicate that our optimized You Only Look Once v5 (YOLOv5) model achieves the highest accuracy of the four methods with an increase of 6.9% points in Average Precision (AP) compared to the baseline. Moreover, we show the real-time capability of the model. The trained models are published on GitHub: hups://***/IN-Friederichlairport_fence_inspection.

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