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Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

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arXiv 2022年

作者： Bauer, Rebecca Mühlpfordt, Tillmann Ludwig, Nicole Hagenmeyer, Veit Institute for Automation und Applied Informatics Karlsruhe Institute of Technology Germany Cluster of Excellence Machine Learning: New Perspectives for Science University of Tübingen Germany

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, CC BY.

关键词： Acoustic generators

A UNIVERSAL SYNTHETIC DATASET FOR MACHINE LEARNING ON SPECTROSCOPIC DATA

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arXiv 2022年

作者： Schuetzke, Jan Szymanski, Nathan J. Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Materials Science & Engineering UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial spectra designed to represent experimental measurements from techniques including X-ray diffraction, nuclear magnetic resonance, and Raman spectroscopy. The dataset generation process features customizable parameters, such as scan length and peak count, which can be adjusted to fit the problem at hand. As an initial benchmark, we simulated a dataset containing 35,000 spectra based on 500 unique classes. To automate the classification of this data, eight different machine learning architectures were evaluated. From the results, we shed light on which factors are most critical to achieve optimal performance for the classification task. The scripts used to generate synthetic spectra, as well as our benchmark dataset and evaluation routines, are made publicly available to aid in the development of improved machine learning models for spectroscopic analysis. © 2022, CC BY.

关键词： Spectroscopic analysis

Cyber-Physical Event Reasoning for Distributed Energy Resources on the Case of a Pv-Battery System

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SSRN

SSRN 2023年

作者： Müller, Nils Bao, Kaibin Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Campus North Building 445 Eggenstein-Leopoldshafen76344 Germany

The widespread adoption of internet-connected and remotely controllable solar plants, battery storages, and other electric devices renders coordinated cyber-physical attacks against such distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The cyber-physical event reasoning system CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need for historical attack samples. However, CyPhERS has been demonstrated on a generic cyber-physical laboratory testbed, considering human evaluation of event signatures. This work extends applicability of CyPhERS to DER monitoring on the case of a real PV-battery system affected by several cyber and cyber-physical attack types. In this context, an automated signature evaluation system is realized, and the concept adapted to specificities of DERs, such as weather and consumer-induced volatility. The results demonstrate that CyPhERS can be applied for online DER monitoring, providing information on root causes and physical impacts of both known attack strategies and unknown event types in an automated fashion. © 2023, The Authors. All rights reserved.

关键词： Energy resources

CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response

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arXiv 2023年

作者： Müller, Nils Bao, Kaibin Matthes, Jörg Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Building 445 Campus North Eggenstein-Leopoldshafen76344 Germany

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and bench-marked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types. © 2023, CC BY.

关键词： Cyber Physical System

Data-driven copy-paste imputation for energy time series

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arXiv 2021年

作者： Weber, Moritz Turowski, Marian Çakmak, Hüseyin K. Mikut, Ralf Kühnapfel, Uwe Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

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

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Classification of Bundle Branch Blocks with QRS Templates Ex...

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

Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI

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arXiv 2023年

作者： Titz, Maurizio Pütz, Sebastian Witthaut, Dirk Jülich52428 Germany University of Cologne Institute for Theoretical Physics Zülpicher Str. 77 Cologne50937 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

The transition to a sustainable energy supply challenges the operation of electric power systems in manifold ways. Transmission grid loads increase as wind and solar power are often installed far away from the consumers. In extreme cases, system operators must intervene via countertrading or redispatch to ensure grid stability. In this article, we provide a data-driven analysis of congestion in the German transmission grid. We develop an explainable machine learning model to predict the volume of redispatch and countertrade on an hourly basis. The model reveals factors that drive or mitigate grid congestion and quantifies their impact. We show that, as expected, wind power generation is the main driver, but hydropower and cross-border electricity trading also play an essential role. Solar power, on the other hand, has no mitigating effect. Our results suggest that a change to the market design would alleviate congestion. Copyright © 2023, The Authors. All rights reserved.

关键词： Solar energy

Trust in Robot Self-Defense: People Would Prefer a Competent, Tele-Operated Robot That Tries to Help*

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Trust in Robot Self-Defense: People Would Prefer a Competent...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Eduardo Kochenborger Duarte Masahiro Shiomi Alexey Vinel Martin Cooney School of Information Technology Halmstad University Halmstad Sweden Interaction Science Laboratories (ISL) Advanced Telecommunications Research Institute International (ATR) Kyoto Japan Karlsruhe Institute of Technology (KIT) Institute of Applied Informatics and Formal Description Methods (AIFB) Karlsruhe Germany

Motivated by the expectation that robot presence at crime scenes will become increasingly prevalent, the question arises of how they can protect humans in their care or vicinity. The current paper delves into the concept of “robot self-defense” and explores whether a robot should be tele-operated or autonomous, and how humans perceive imperfections in robot performance. To gain insight into how people feel, an online survey was conducted with 180 participants, who watched six videos of a robot defending a victim. The study provides insights into trust in human-robot interactions and sheds light on the complex dynamics involved in robot self-defense. The results indicate that people found a tele-operated robot to be more accepted, and that attempting to help but failing is more acceptable than just observing.

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Linear Layouts of Complete Graphs 29th

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Linear Layouts of Complete Graphs

29th International Symposium on Graph Drawing and Network Visualization, GD 2021

作者： Felsner, Stefan Merker, Laura Ueckerdt, Torsten Valtr, Pavel Institute of Mathematics Technische Universität Berlin Berlin Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783030929305

A page (queue) with respect to a vertex ordering of a graph is a set of edges such that no two edges cross (nest), i.e., have their endpoints ordered in an abab-pattern (abba-pattern). A union page (union queue) is a vertex-disjoint union of pages (queues). The union page number (union queue number) of a graph is the smallest k such that there is a vertex ordering and a partition of the edges into k union pages (union queues). The local page number (local queue number) is the smallest k for which there is a vertex ordering and a partition of the edges into pages (queues) such that each vertex has incident edges in at most k pages (queues). We present upper and lower bounds on these four parameters for the complete graph Kn on n vertices. In three cases we obtain the exact result up to an additive constant. In particular, the local page number of Kn is n/ 3 ± O(1 ), while its local and union queue number is (1-1/2)n±O(1). The union page number of Kn is between n/ 3 - O(1 ) and 4 n/ 9 + O(1 ). © 2021, Springer Nature Switzerland AG.

关键词： Graph theory