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Implementation of Digital Shadow Concept in the Field of Industrial automation 11

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Implementation of Digital Shadow Concept in the Field of Ind...

11th International Scientific Conference on Computer Science, COMSCI 2023

作者： Peter, Radovan Juhas, Martin Juhasova, Bohuslava Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Faculty of Materials Science and Technology in Trnava Trnava Slovakia

ISBN: (纸本)9798350325256

We are currently witnessing a gradual increase in interest in digital shadow and digital twin solutions in the field of industrial automation. These solutions require advanced approaches in the process of their creation and are gradually becoming an integral part of industrial automation. This paper focuses on the creation of a digital shadow of a physical robotic cell and progressively goes through all the phases from the analysis of communication problems through the design of possible solutions to the actual implementation of one of the proposed solutions. The connection between the virtual robotic cell and the industrial camera is based on the processing of the data sent by the camera located above the physical robotic cell. This data is processed by a program in the PLC and then transferred to the virtual environment of the digital shadow by communication between the two OPC servers. This paper provides a more detailed overview of one possible technique for connecting the virtual environment with the physical robotic cell. © 2023 IEEE.

关键词： Cameras

Suitability of MQTT and REST Communication Protocols for AIoT or IIoT Devices Based on ESP32 S3 1

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Nemlaha, Eduard Střelec, Peter Horák, Tibor Kováč, Szabolcs Tanuška, Pavol Institute of Applied Informatics Automation and Mechatronics Faculty of Materials Science and Technology in Trnava Slovak University of Technology Bratislava Trnava Slovakia

ISBN: (数字)9783031214356

ISBN: (纸本)9783031214349

Internet of Things (IoT) devices are not just simple devices anymore. The introduction of industry IoT (IIoT) and Artificial Intelligence of Things (AIoT) creates new challenges in terms of communication. The presented comparation shows the advantages and disadvantages of two communication protocols, which are suitable for new coming generation of IoT devices. Message Queuing Telemetry Transport (MQTT) and Representational State Transfer (REST) can provide extended functions for data acquisition and they offer different options in terms of data transfer. A demonstration of the integration of IoT devices is shown, based on both protocols and there are classified from the functional perspective in terms of Industry 4.0. The experiment is built on the ESP32 S3 development board and data are processed by Node-Red flow. Data are acquired and saved into relation database. Verification of data transfer is performed and is visible on the dashboard. The transfer of data and connections were also recorded. The IoT device sends three data types, which are produced by sensors. Data types were selected to demonstrate a possibility how to work with standard data types like char variables or numerical values but main topic is to show how to transfer binary or complex data structures like JavaScript Object Notation (JSON). The experiment also demonstrates how communication protocol can impact the power consumption of a device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： AIoT ESP32 S3 IoT MQTT REST

Machine Learning-Based Feature Selection for Intrusion Detection Systems in IEC 61850-Based Digital Substations

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Machine Learning-Based Feature Selection for Intrusion Detec...

IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Ahmad Eynawi Aneeqa Mumrez Ghada Elbez Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) KASTEL Security Research Labs (SRL) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

With the evolution in attack surfaces, smart grid complexity, and adversary models, conventional defense solutions in the smart grid domain have become less effective in providing adequate protection. In recent years, machine learning-based intrusion detection systems have been a current research topic, offering a promising and reliable defense against advanced threats. This work focuses on the development of a machine learning-based feature selection algorithm for a robust and efficient detection of attacks specifically targeting the Sampled Values and Manufacturing Message Specification protocols widely used in IEC 61850-based substations. For dataset generation, we implement several cyber-physical attacks such as injection, replay, time delay, and data modification attacks against a hardware-based substation test environment at KASTEL Security Lab Energy. The generated datasets are used to build and evaluate the developed feature selection algorithm. Our findings show that even a small number of carefully selected features can lead to more efficient and accurate anomaly detection. Considering the scarcity of datasets for smart grids, we have also released our datasets to the community for further analysis and evaluation of data-driven defense mechanisms for digital substations.

关键词： Substations Protocols Intrusion detection Feature extraction Smart grids Manufacturing Security Reliability IEC Standards Protection

Line Management in the Energy Packet Grid 24

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Line Management in the Energy Packet Grid

Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems

作者： Marcel Weßbecher Friedrich Wiegel Sophie Xing An Klemens Schneider Dominik Schulz Martina Zitterbart Marc Hiller Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Germany Institute of Telematics (TM) Karlsruhe Institute of Technology (KIT) Germany Institute of Electrical Engineering (ETI) Karlsruhe Institute of Technology (KIT) Germany

ISBN: (纸本)9798400704802

This article introduces the innovative concept of Line Management (LM) in Energy Packet Grids (EP grids), an approach that aims to address the challenges posed by the increasing deployment of renewable energy technologies in the power grid driven by power electronics. The traditional power grid, originally designed with a top-down architecture, faces new complexities due to the distributed, bottom-up, and intermittent nature of renewable energy generation. The article explores the concept of the Energy Packet Grid, which segments the power grid into EP cells and manages power flows in discrete packets. Central to this system is the Line Manager, a software component critical to managing the transport capacity of transmission lines by orchestrating the transfer of energy packets. The paper outlines the architecture and functional aspects of the Line Manager, emphasizing its role in ensuring a stable operation of future power grids. It also discusses various methodologies for grid state calculations, including current, power, and mixed-mode transfers. The effectiveness of different calculation methods is evaluated, highlighting the importance of accurate and timely decision-making in grid management. Overall, the results contribute to ongoing research in congestion management and present a detailed description of the Line Manager concept and implementation in the context of EP grids.

关键词： Congestion Management Energy Internet Energy Packet Grid Grid State Calculation Line Management

Threat Modeling to Design a Decentralized Health Data Management Application

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Threat Modeling to Design a Decentralized Health Data Mana...

International Conference on Information technology and Systems, ICITS 2023

作者： Erler, Christina Hu, Shanshan Danelski, Alexa Stork, Wilhelm Sunyaev, Ali Gersch, Martin Embedded Systems and Sensors Engineering FZI Research Center for Information Technology Karlsruhe76131 Germany Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Information Systems Free University of Berlin Berlin14195 Germany Institute for Information Processing Technologies Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9783031332609

Decentralized applications are increasingly recognized as efficient tools to overcome security challenges in the management of highly sensitive health information. However, research still lacks the understanding of how to address the complex security challenges in the design of health data management applications (HDMAs). In this study, we structurally analyzed security requirements of health data management systems to improve the efficiency of their design. We leveraged threat modeling to inform the design of HDMAs and proposed the system architecture accordingly to facilitate the security of interorganizational health data exchange. Our results contribute to the exaptation of threat modeling to the innovative design of decentralized applications for health data management and to the exploration of benefits of decentralization for healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

Occupant-Oriented Demand Response with Multi-Zone Thermal Building Control

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arXiv 2023年

作者： Frahm, Moritz Dengiz, Thomas Zwickel, Philipp Maaß, Heiko Matthes, Jörg Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Eggenstein-Leopoldshafen Germany Karlsruhe Institute of Technology Institute for Industrial Production Karlsruhe Germany

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants’ Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a heuristic control approach from the literature (PC), and a conventional hysteresis-based two-point control as upper and lower benchmarks. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters concerning the electricity costs and OTS. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems. © 2023, CC BY.

关键词： Model predictive control

Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks

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arXiv 2023年

作者： Phipps, Kaleb Heidrich, Benedikt Turowski, Marian Wittig, Moritz Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute for Technology Institute for Automation and Applied Informatics Eggenstein-Leopoldshafen76344 Germany

In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these deterministic forecasts into probabilistic forecasts is often challenging and based on numerous assumptions that may not hold in real-world situations. Therefore, the present article proposes a novel approach for creating probabilistic forecasts from arbitrary deterministic forecasts. In order to implement this approach, we use a conditional Invertible Neural Network (cINN). More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions. Besides showing the mathematical validity of our approach, we empirically show that our approach noticeably outperforms traditional methods for including uncertainty in deterministic forecasts and generally outperforms state-of-the-art probabilistic forecasting benchmarks. © 2023, CC BY.

关键词： Forecasting

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information

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arXiv 2023年

作者： Heidrich, Benedikt Phipps, Kaleb Neumann, Oliver Turowski, Marian Mikut, Ralf Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs. © 2023, CC BY.

关键词： Time series

Open Data-Driven automation of Residential Distribution Grid Modeling with Minimal Data Requirements

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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