Publikationen

2025

• An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration

  Gruman, N., Moses, P., & Jauch, C. (2025). An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration. In 2025 Intermountain Engineering, Technology and Computing (IETC) (S. 6). Orem, UT, USA: IEEE. http://doi.org/10.1109/IETC64455.2025.11039473 (Original work published Juni 2025)

  Abstract

  anzeigen ▸

  The rise of wind power in bulk electric systems creates a growing need to model, understand, and characterize responses to various sources of disruption. Despite the numerous advantages of computer models, physical emulators have enormous benefits in characterizing dynamic magnetic electromechanical phenomena which are unexpected or difficult for models to process accurately. The development of an experimental apparatus for physical wind farm emulation is described. The control and operation of an induction motor-generator setup for mimicking wind generator dynamics are demonstrated. Recommendations for further application of this emulator are also given.

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Jauch, Clemens

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind
    • Netzintegration

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

  Filter Tags

  • 2025
  • Conference Paper
  • • Noah Gruman
    • Paul Moses
    • Prof. Dr. Clemens Jauch
  • • Emulator
    • grid connection
    • induction machine
    • inverter, renewables
    • wind energy

• State estimation using LSTMs trained on synthetic data and validated with real measurements

  Geisler, J. (2025). State estimation using LSTMs trained on synthetic data and validated with real measurements. Namtes: WESC 2025. http://doi.org/10.5281/zenodo.15830516 (Original work published Juni 2025)

  Abstract

  anzeigen ▸

  This study represents a novel contribution by demonstrating that LSTM-based estimators can replace traditional state estimation pipelines in wind turbine applications. By eliminating the need for detailed modeling and manual tuning, this approach opens up new opportunities for data-driven estimation in renewable energy systems.

  Unlike conventional estimators based on linear or nonlinear physical models, the proposed method trains LSTM networks to learn the mapping from inputs and outputs to internal state estimates using only simulation data. Such data can be generated in large volumes with complete access to all internal states, leveraging high-fidelity turbine simulation tools. The trained LSTM functions as a complete estimator, replacing both the predictor and corrector stages typically found in Kalman filter structures.

  The approach is evaluated across a variety of estimation tasks, from rotor effective wind speed (REWS) to more complex quantities such as blade deflections and wind shear coefficients. Results demonstrate that LSTMs trained entirely on simulation data can generalize well to real-world measurements, achieving estimation quality comparable to that of EKFs. Moreover, the method proves flexible and scalable to different input-output combinations without extensive manual modeling.

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Geisler, Jens

  • Forschungsprojekt

    • Adaptive Betriebsstrategien für bestehende Windenergieanlagen

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

  Filter Tags

  • 2025
  • Miscellaneous
  • • Jens Geisler
  • • state estimation
    • observer
    • kalman filter
    • neural network
    • LSTM
    • high fidelity model
    • real measurements

• Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine

  Goga, A., Jauch, C., Alhrshy, L., Gagel, A., & Kloft, P. (2025). Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine. In NEIS Conference 2024 (S. 184–191). Hamburg: VDE Verlag GmbH. http://doi.org/10.30420/566464025 (Original work published März 2025)

  Abstract

  anzeigen ▸

  Researchers in the wind sector continuously aim to lower mechanical and structural loads on wind turbines to reduce the amount of material used in their components. One such innovative concept that is being investigated here is the integration of a flywheel system into rotor blades. In contrast to its intended aim of reducing the material usage and thereby CO2 emissions, the flywheel system in its implementation requires additional materials, which increases the carbon footprint considerably. Hence, to achieve a net CO2 saving, the CO2 expenditure of the additional components needs to be saved by the system as a minimum. The purpose of this work is to quantify the rise in CO2 emissions, caused by the installation of the flywheel system, relative to the original design. Thereby, the flywheel system’s minimal performance requirement for an exemplary wind turbine is defined.

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Goga, Abhinay
    • Jauch, Clemens
    • Alhrshy, Laurence

  • Forschungsprojekt

    • Ressourceneffizienz in der Windenergie

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

  Filter Tags

  • 2025
  • Conference Paper
  • • Abhinay Goga
    • Prof. Dr. Clemens Jauch
    • Laurence Alhrshy
    • Andreas Gagel
    • Peter Kloft
  • • Wind Turbine
    • Variable inertia flywheel
    • Increase In CO2 Emissions
    • Construction Materials

• On the road to interactive LLM-based systematic mapping studies

  Petersen, K., & Gerken, J. M. (2025). On the road to interactive LLM-based systematic mapping studies. Information and Software Technology, 178. (Original work published Februar 2025)

  • Suche auf Google Scholar

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Petersen, Kai
    • Gerken, Jan

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Fachbereich

    • Fachbereich 4: Wirtschaft

  Filter Tags

  • 2025
  • Journal Article
  • • Kai Petersen
    • Jan Gerken
  • • Large language models
    • Systematic mapping studies
    • GPT

• Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems

  Reese, L., Rettig, A., Jauch, C., Domin, R. J., & Karshüning, T. (2025). Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems. Energies, 18, 17. http://doi.org/10.3390/en18020418 (Original work published Januar 2025)

  Abstract

  anzeigen ▸

  Due to the energy transition, the future electric power system will face further challenges that affect the functionality of the electricity grid and therefore the security of supply. For this reason, this article examines the future frequency stabilisation in a 100% renewable electric power system. A focus is set on the provision of inertia and frequency containment reserve. Today, the frequency stabilisation in most power systems is based on synchronous generators. By using grid-forming frequency converters, a large potential of alternative frequency stabilisation reserves can be tapped. Consequently, frequency stabilisation is not a problem of existing capacities but whether and how these are utilised. Therefore, in this paper, a collaborative approach to realise frequency stabilisation is proposed. By distributing the required inertia and frequency containment reserve across all technologies that are able to provide it, the relative contribution of each individual provider is low. To cover the need for frequency containment reserve, each capable technology would have to provide less than 1% of its rated power. The inertia demand can be covered by the available capacities at a coverage ratio of 171% (excluding wind power) to 217% (all capacities). As a result, it is proposed that provision of frequency stabilisation is made mandatory for all capable technologies. The joint provision distributes the burden of frequency stabilisation across many participants and hence increases redundancy. It ensures the stability of future electricity grids, and at the same time, it reduces the technological and economic effort. The findings are presented for the example of the German electricity grid.

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Jauch, Clemens

  • Forschungsprojekt

    • Inno!Nord: Innovationslabor: Speicher zur Nutzung erneuerbarer Energien im echten Norden

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

  Filter Tags

  • 2025
  • Journal Article
  • • Lisanne Reese
    • Arne Rettig
    • Prof. Dr. Clemens Jauch
    • Richard Domin
    • Tom Karshüning
  • • electric power system
    • energy transition
    • frequency containment reserve
    • frequency stabilisation
    • inertia
    • renewable energy
    • synchronous stability

• Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

  Banerjee, S. ", Bertram, C. A., Ammeling, J., Weiss, V., Conrad, T., Klopfleisch, R., … Aubreville, M. (2025). Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma. In Bildverarbeitung für die Medizin 2025 (S. 260–265). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-47422-5_56

  Abstract

  anzeigen ▸

  Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

  Filter Tags

  • 2025
  • Conference Paper
  • • Sweta Banerjee
    • Christof Bertram
    • Jonas Ammeling
    • Viktoria Weiss
    • Thomas Conrad
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Katharina Breininger
    • Marc Aubreville

• REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods

  Amidi, M., Horstmann, A. C., Mertineit, J. A., & Wallbaum, T. (2025). REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods. In Proceedings of the 2025 Mensch und Computer 2025 (S. 694–699).

  • Suche auf Google Scholar

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Wallbaum, Torben

  Filter Tags

  • 2025
  • Conference Paper
  • • Maryam Amidi
    • Aike Horstmann
    • Jana Mertineit
    • Torben Wallbaum

• Investigation of Class Separability within Object Detection Models in Histopathology

  Ammeling, J., Ganz, J., Wilm, F., Breininger, K., & Aubreville, M. (2025). Investigation of Class Separability within Object Detection Models in Histopathology. IEEE Transactions on Medical Imaging, 1–1. http://doi.org/10.1109/TMI.2025.3560134

  • Suche auf Google Scholar
  • DOI Verweis

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

  Filter Tags

  • 2025
  • Journal Article
  • • Jonas Ammeling
    • Jonathan Ganz
    • Frauke Wilm
    • Katharina Breininger
    • Marc Aubreville
  • • Feature extraction
    • Object detection
    • Measurement
    • Adaptation models
    • histopathology
    • Training
    • Complexity theory
    • YOLO
    • Location awareness
    • Data mining
    • object detection
    • latent space discrimination

• MeasureTip: Towards a Finger-Worn Measurement Device

  Olberding, S., & Wallbaum, T. (2025). MeasureTip: Towards a Finger-Worn Measurement Device. In Proceedings of the 2025 Mensch und Computer 2025 (S. 673–677).

  • Suche auf Google Scholar

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Wallbaum, Torben

  Filter Tags

  • 2025
  • Conference Paper
  • • Simon Olberding
    • Torben Wallbaum

• Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

  Bertram, C. A. ", Weiss, V., Donovan, T. A., Banerjee, S., Conrad, T., Ammeling, J., … Aubreville, M. (2025). Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br). In "Bildverarbeitung für die Medizin 2025" (S. 113–118). "Wiesbaden": "Springer Fachmedien Wiesbaden".

  Abstract

  anzeigen ▸

  "Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4\%) and 2,888 normal MFs (77.6\%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance.We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split."

  • Suche auf Google Scholar

  Exportformate

  • BibTeX
  • EndNote X3 XML
  • EndNote 7 XML
  • EndNote tagged
  • RIS

  Weiterführende Links

  anzeigen ▸

  • Personenseiten

    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research
    • CIVU – Center for Interaction, Visualization and Usability

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Digitale Transformation
    • Human-Computer-Interaction
    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

  Filter Tags

  • 2025
  • Conference Paper
  • • Christof Bertram
    • Viktoria Weiss
    • Taryn Donovan
    • Sweta Banerjee
    • Thomas Conrad
    • Jonas Ammeling
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Marc Aubreville