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International Conference on System Reliability and Science (ICSRS)

作者： Mohammed Ramadan Ghada Elbez Veit Hagenmeyer KASTEL Security Research Labs Institute of Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen Germany

Smart Grids (SG) are potential replacements for older power grids, capable of adapting and distributing energy based on demand. The recent technological advances and the increased dependence on electrical energy are all factors in the increased need for reliable and efficient energy systems. The complexity of the smart grid arises from using various components and the high requirements for real-time reliable two-way communication. Accordingly, this represents additional security and privacy challenges. In this paper, we proposed a security and efficient certificateless signcryption scheme with flexible verifiability properties using equality test techniques for Advanced Metering Infrastructure (AMI) within the SG; abbreviated CL-SG. The proposed scheme provides privacy-preserving, confidentiality, data integrity, verifiability, and reduces the level of trust in the third party (e.g., central unit, substation, data concentrator, etc.). The performance evaluation regarding security and complexity analysis shows that the proposed scheme is secure and efficient with good functionality to be fully adopted and implemented within SG. Also, an extended future work is presented to add more flexibility and security properties and features to the current proposed model.

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Inverse Methods: How Feasible are Spatially Low-Resolved Capacity Expansion Modeling Results When Disaggregated at High Resolution?

SSRN

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SSRN 2023年

作者： Frysztacki, Martha Maria Hagenmeyer, Veit Brown, Tom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Department of Digital Transformation in Energy Systems

Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally ***, the simplification mixes sites with different renewable features while removing transmission lines that can cause *** a consequence, the results may represent an infeasible system when the capacities are fed back into higher spatial *** far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is *** is challenging since there is no unique way to invert the *** proceed in two stages. First, we propose three methods to disaggregate spatially low-resolved results: (a) uniformly distribute the regional result across its original regions, (b) re-optimising each region separately (c) a novel approach that minimises the ``excess electricity''.Second, we investigate the resulting highly-resolved models' feasibility by running an operational dispatch. While re-optimising yields the best results, our custom inverse-method provides comparable results for less computational ***-wise, our results strengthen that modeling every country by a single region is *** that, we find that results obtained from state-of-the-art reduced models with 100-200 regions for Europe still yield 3-7% of loadshedding, depending on model resolution and inverse method. © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow with Distributed Generators Using a Machine Learning-Based Approach

TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow w...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Burak Dindar Can Berk Saner Hüseyin Kemal Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

The integration of distributed generators (DGs) into the electrical grid has introduced new dimensions in power system management: Enhancing efficiency and operational flexibility of the grid requires taking into account the DGs located at the distribution level in dispatch decisions, while also considering the operational constraints of both distribution and transmission levels. However, the centralization of cooperation between transmission system operators (TSOs) and distribution system operators (DSOs) is troublesome due to privacy concerns of both parties. In present paper, we introduce a Machine Learning-Based Privacy-Preserving Optimal Power Flow (MLB-PP-OPF) framework. This method enables the seamless inclusion of DGs at the distribution level in optimal power flow calculations, all while adhering to the operational limits of both systems and maintaining data privacy. The proposed approach leverages three machine learning models to map the distribution system variables to active and reactive power flows at the point of common coupling, and to determine the feasible operating region of the distribution system in a privacy-preserving manner. Comparative case studies, performed on the coupled IEEE 30-bus transmission and 33-bus distribution test systems, demonstrate that the results achieved using MLB-PP-OPF align almost perfectly with those obtained through classical OPF, while maintaining data privacy.

关键词： Couplings Data privacy Reactive power Privacy Machine learning Generators Data models

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Design of transformation initiatives implementing organisational agility: an empirical study

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SN Business and Economics 2021年第6期1卷 1-28页

作者： Kovynyov, Ivan Buerck, Axel Mikut, Ralf Imperial College Business School London United Kingdom Kobaltblau Management Consultants GmbH Munich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

This study uses 125 responses from companies of all sizes predominantly headquartered in Germany, Switzerland, France and UK to reveal perceptions of the drivers of organisational agility. It further investigates current understanding of managing principles of multiple organisational dimensions such as culture, values, leadership, organisational structure, processes and others to achieve greater organisational agility. The data set is disaggregated into four major profiles of agile organisations: laggards, execution specialists, experimenters, and leaders. The approach to agile transformation is analysed by each of those profiles. While the positive effect from a more holistic approach is confirmed, leaders tend to focus more on processes and products rather than project work. Respondents perceive that IT, product development and research are most agile functions within their organisations, while human resources, finance and administration are considered being not agile. Furthermore, organisations with higher levels of organisational agility tend to use more than one agile scaling framework. Implications on theories of agile transformations and organisational design are discussed. © The Author(s) 2021.

关键词： Agile Agile organisations Agile transformation Organisational agility Organisational design

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ciscNet - A Single-Branch Cell Instance Segmentation and Classification Network

arXiv

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arXiv 2022年

作者： Böhland, Moritz Neumann, Oliver Schilling, Marcel P. Reischl, Markus Mikut, Ralf Löffler, Katharina Scherr, Tim Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and comparability of segmentation and classification methods for histopathological images. In this contribution, we describe our CoNIC Challenge 2022 method ciscNet to segment, classify and count cell nuclei, and report preliminary evaluation results. Our code is available at https://***/ciscnet/ciscnet-conic-2022. Copyright © 2022, The Authors. All rights reserved.

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Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs

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Current Directions in Biomedical Engineering 2021年第2期7卷 582-585页

作者： Welle, Hannes Nagel, Claudia Loewe, Axel Mikut, Ralf Dössel, Olaf Institute of Biomedical Engineering KIT Fritz-Haber-Weg 1 Karlsruhe Germany Institute for Automation and Applied Informatics KIT Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen Germany

Being non-invasive, cheap and widely available, the 12-lead electrocardiogram (ECG) is a standard method to assess cardiac function. Still, its reliable interpretation requires specialized knowledge and experience, rendering a second opinion valuable. We evaluated the performance of machine learning based classification of 11,705 healthy and bundle branch block 12-lead ECGs from 3 open databases. For each lead of the ECG signal, a representative QRS-complex template was extracted automatically. Principal component analysis (PCA) was applied to the concatenated, normalized and rescaled QRS signals to reduce their dimensionality. Multilayer perceptron and support-vector machine classifiers were trained using the principal components of weighted and non-weighted QRS template signals as input data. Classifiers achieved F1 scores between 0.92 and 0.96 on the test set for different input configurations. Anomaly based weighting slightly improved the performance of the classifiers. Neither class-wise PCA for feature extraction nor adding information on sex, gender and electrical heart axis to the input data yielded considerable improvement of the F1 scores. The achieved classification accuracy is similar to deep learning classifier performances and should generalize robustly to other ECG datasets. Our results suggest that this simple and well interpretable approach based on morphological signal characteristics is suitable for automatically and non-invasively identifying bundle branch block pathologies in clinical or smart electronics contexts. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Electrocardiograms

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Comparative Study on Smart Grid Security Testbeds Using MITRE ATT&CK Matrix

Comparative Study on Smart Grid Security Testbeds Using MITR...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Aneeqa Mumrez Muhammad M. Roomi Heng Chuan Tan Daisuke Mashima Ghada Elbez Veit Hagenmeyer Institute for Automation and Applied Informatics KASTEL Security Research Labs Karlsruhe Institute of Technology Illinois Advanced Research Center at Singapore (IARCS)

The increasing use of information and communication technologies has led to increasing threats on critical infrastructure like smart grids. Owing to confidentiality issues and difficulties in experimenting cyberattacks on a real environment, testbeds reflecting the characteristics of smart grids have been developed to enable attack experiments and cybersecurity research. In fact, different implementations of smart grid testbeds exist, each with its own challenges and limitations. The comparative study we present here justifies the usability of each type of testbed for different experiments. We begin our study with a selection of state-of-the-art smart grid testbeds, which can be classified into physical, hybrid, and virtual. We then present an evaluation based upon several qualitative metrics and MITRE ATT&CK matrix to demonstrate the coverage of all tactics and techniques to guide about the selection of testbed according to security needs. Finally, we explain applicable tactics and techniques for one of the testbeds through an attack case study to highlight current cybersecurity challenges faced by smart grids.

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Input and state constrained inverse optimal control with application to power networks

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IFAC-PapersOnLine 2023年第2期56卷 5451-5456页

作者： Taouba Kaouther Jouini Zhiyong Sun Venkatraman Renganathan Veit Hagenmeyer Institute of Automation and Applied Informatics (IAI) at Karlsruhe Institute of Technology Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Automatic Control LTH Lund University Sweden

We study input and state constrained inverse optimal control problems starting from a stabilizing controller with a control Lyapunov function, where the goal is to make the controller an explicit solution of the resulting constrained optimal control problem. For an appropriate cost design and initial states for which a sublevel set of the Lyapunov function is contained in the state constraint set and the initial input lies on an ellipsoid inside the input constraint set, we show that the stabilizing controller solves the constrained optimal control problem. Compared to the state-of-the-art, we avoid solving nonlinear optimization problems evaluated pointwise, i.e., for every state, or in a repetitive fashion, i.e., at each time step. We apply our theoretical results to study the angular droop control studied in (Jouini et al., 2022) of an inverter-based power network. For this, we accommodate the constraints on the angle and power generation and exemplify our approach through a two-inverter case study.

关键词： Optimal Control Non-Linear Control Systems Networked Systems Power and Energy Systems

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AUTOPV: AUTOMATED PHOTOVOLTAIC FORECASTS WITH LIMITED INFORMATION USING AN ENSEMBLE OF PRE-TRAINED MODELS

arXiv

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arXiv 2022年

作者： Meisenbacher, Stefan Heidrich, Benedikt Martin, Tim Mikut, Ralf Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual PV plants includes two challenges: First, information about the PV mounting configuration (i.e. inclination and azimuth angles) is often missing. Second, for new PV plants, the amount of historical data available to train a forecasting model is limited (cold-start problem). We address these two challenges by proposing a new method for day-ahead PV power generation forecasts. The proposed AutoPV method is a weighted ensemble of forecasting models that represent different PV mounting configurations. This representation is achieved by pre-training each forecasting model on a separate PV plant and by scaling the model’s output with the peak power rating of the corresponding PV plant. To tackle the cold-start problem, we initially weight each forecasting model in the ensemble equally. To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error. The proposed AutoPV method is advantageous as the unknown PV mounting configuration is implicitly reflected in the ensemble weights, and only the PV plant’s peak power rating is required to re-scale the ensemble’s output. The ensemble approach also allows to represent PV plants with panels distributed on different roofs with varying alignments, as these mounting configurations can be reflected proportionally in the weighting. Additionally, the required computing memory is decoupled when scaling AutoPV to several hundreds of PV plants, which is beneficial in Smart Grid environments with limited computing capabilities. For a real-world data set with 11 PV plants, the accuracy of AutoPV is comparable to an individual model trained on two years of data and outperforms an incrementally trained individual model. © 2022,

关键词： Forecasting

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AUTOMATED GENERATION OF LARGE-SCALE DISTRIBUTION GRID MODELS BASED ON OPEN DATA AND OPEN SOURCE SOFTWARE USING AN OPTIMIZATION APPROACH

arXiv

引用

arXiv 2022年

作者： Çakmak, Hüseyin K. Janecke, Luc Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen 76344 Germany

The increasing share of renewable energy sources on distribution grid level as well as the emerging active role of prosumers lead to both higher distribution grid utilization, and at the same time greater unpredictability of energy generation and consumption. This poses major problems for grid operators in view of, e.g., voltage stability and line (over)loading. Thus, detailed and comprehensive simulation models are essential for planning future distribution grid expansion in view of the expected strong electrification of society. In this context, the contribution of the present paper is a new, more refined method for automated creation of large-scale detailed distribution grid models based solely on publicly available GIS and statistical data. Utilizing the street layouts in Open Street Maps as potential cable routes, a graph representation is created and complemented by residential units that are extracted from the same data source. This graph structure is adjusted to match electrical low-voltage grid topology by solving a variation of the minimum cost flow linear optimization problem with provided data on secondary substations. In a final step, the generated grid representation is transferred to a DIgSILENT PowerFactory model with photovoltaic systems. The presented workflow uses open source software and is fully automated and scalable that allows the generation of ready-to-use distribution grid simulation models for given 20 kV substation locations and additional data on residential unit properties for improved results. The performance of the developed method with respect to grid utilization is presented for a selected suburban residential area with power flow simulations for eight scenarios including current residential PV installation and a future scenario with full PV expansion. Furthermore, the suitability of the generated models for quasi-dynamic simulations is shown. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load flow

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