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2025

Evaluation and Lessons Learned from the Lidar-Assisted Control Summer Games 2024

Schlipf, D., Weich, S., Raach, S., & Guo, F. (2025). Evaluation and Lessons Learned from the Lidar-Assisted Control Summer Games 2024. http://doi.org/10.5281/zenodo.16105260 (Original work published 2026)
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Schlipf, David
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2025

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David Schlipf

Simon Weich

Steffen Raach

Feng Guo
First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024

Schlipf, D. (2025). First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024. Zenodo. http://doi.org/10.5281/zenodo.14778978 (Original work published 2026)
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Schlipf, David

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Fachbereich 2: Energy and Life Science
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2025

Miscellaneous

David Schlipf
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

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.
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Geisler, Jens

Forschungsprojekt

Adaptive Betriebsstrategien für bestehende Windenergieanlagen

Profilbereich

Erneuerbare Energien und nachhaltige Entwicklung

Forschungsfeld

Wind

Fachbereich

Fachbereich 2: Energy and Life Science
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2025

Miscellaneous

Jens Geisler

state estimation

observer

kalman filter

neural network

LSTM

high fidelity model

real measurements
Vortrag: Extradimensionen und mikroskopische Schwarze Löcher

Vest, A. (2025). Vortrag: Extradimensionen und mikroskopische Schwarze Löcher.
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Vest, Anja

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Fachbereich 2: Energy and Life Science
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2025

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Anja Vest

2024

Lidar-Assisted Control by Retrofitting Wind Farms

Schlipf, D. (2024). Lidar-Assisted Control by Retrofitting Wind Farms. http://doi.org/10.5281/zenodo.15290994 (Original work published 2026)
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Schlipf, David
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2024

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David Schlipf
Next generation lidar-assisted control

Raach, S., Burth, C., & Schlipf, D. (2024). Next generation lidar-assisted control. http://doi.org/10.5281/zenodo.15290994 (Original work published 2026)
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Schlipf, David
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2024

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Steffen Raach

Chris Burth

David Schlipf
Study on the impact of wind models and data processing on lidar-assisted control performance

Fu, W., Schlipf, D., Raach, S., White, S., Pitter, M., & Slinger, C. (2024). Study on the impact of wind models and data processing on lidar-assisted control performance. http://doi.org/10.5281/zenodo.12666568 (Original work published 2026)
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Schlipf, David
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2024

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Wei Fu

David Schlipf

Steffen Raach

Steven White

Mark Pitter

Chris Slinger
Auslegung und Einsatz von Großwärmepumpen in verschiedenen multivalenten Wärmeversorgungssystemen basierend auf MILP-Optimierung

Fritz, M., Freißmann, J., & Tuschy, I. (2024). Auslegung und Einsatz von Großwärmepumpen in verschiedenen multivalenten Wärmeversorgungssystemen basierend auf MILP-Optimierung. Zenodo. http://doi.org/10.5281/zenodo.15752964 (Original work published 2026)
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Fritz, Malte

Freißmann, Jonas

Tuschy, Ilja
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2024

Miscellaneous

Malte Fritz

Jonas Freißmann

Ilja Tuschy
Development, testing and integration of lidar- assisted control strategies within the ABBA project

Schlipf, D., Schedat, M., & Weich, S. (2024). Development, testing and integration of lidar- assisted control strategies within the ABBA project. http://doi.org/10.5281/zenodo.14500560 (Original work published 2026)
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Schlipf, David
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2024

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David Schlipf

Marcel Schedat

Simon Weich
Contribution of wind energy education to resilience in the face of the climate crisis

Schlipf, D., Soto, G. Q., Rodriguez, H. Z., & Brovia, S. P. (2024). Contribution of wind energy education to resilience in the face of the climate crisis. http://doi.org/10.5281/zenodo.13270425 (Original work published 2026)
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Schlipf, David
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2024

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David Schlipf

Grace Soto

Habib Rodriguez

Santiago Brovia

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2025

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