Resilience of smart integrated energy systems Babazadeh, Davood; Teimourzadeh Baboli, Payam; Mayer, Christoph; Brand, Michael; Becker, Christian; Lehnhoff, Sebastian In: Fathi, M., Zio, E., Pardalos, P.M. (eds): Handbook of Smart Energy Systems. Springer, Cham, 1887-1913 (2023)
Education and Training Needs, Methods, and Tools Kotsampopoulos, P.; Jensen, Tue V.; Babazadeh, Davood; Strasser, Thomas I.; Rikos, E.; Nguyen, V. H.; Tran, Q. T.; Bhandia, R.; Guillo-Sansano, E.; Heussen, Kai; Narayan, Anand; Nguyen, T. L.; Burt, G. M.; Hatziargyriou, N. In: Strasser T., de Jong E., Sosnina M. (eds) European Guide to Power System Testing. Springer, Cham. (2020) Verlags DOI
Experiences with System-Level Validation Approach Baboli, Payam Teimourzadeh; Babazadeh, Davood; Siagkas, D.; Manikas, S.; Anastasakis, K.; Merino, Julia In: Strasser T., de Jong E., Sosnina M. (eds) European Guide to Power System Testing. Springer, Cham. (2020) Verlags DOI
Test Procedure and Description for System Testing Heussen, Kai; Babazadeh, Davood; Degefa, Merkebu Z.; Taxt, H.; Merino, Julia; Nguyen, V. H.; Baboli, Payam Teimourzadeh; Moghim Khavari, A.; Rikos, E.; Pellegrino, L.; Tran, Q. T.; Jensen, Tue V.; Kotsampopoulos, P.; Strasser, Thomas I. In: Strasser T., de Jong E., Sosnina M. (eds) European Guide to Power System Testing. Springer, Cham. (2020) Verlags DOI
Erigrid holistic test description for validating cyber-physical energy systems Heussen, Kai; Steinbrink, Cornelius; Abdulhadi, Ibrahim F.; Van Hoa, Nguyen; Degefa, Merkebu Z.; Merino, Julia; Jensen, Tue V.; Guo, Hao; Gehrke, Oliver; Bondy, Daniel Esteban Morales; Babazadeh, Davood; Andrén, Filip Pröstl; Strasser, Thomas I. Energies 14 (12): 2722 (2019) Verlags DOI
Co-simulation set-up for testing controller interactions in distribution networks Velasquez, Jorge; Castro, Felipe; Babazadeh, Davood; Lehnhoff, Sebastian; Kumm, Thomas; Heuberger, Daniel; Treydel, Riccardo; Lüken, Tim; Garske, Steffen; Hofmann, Lutz Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES 2018)
Modern high-frequency systems benefit massively from machine learning methods. In applications where rule-based algorithms reach their limits, these data-driven approaches enable a significant increase in resolution and accuracy. This is exemplified by current research challenges, namely for the classification of targets in autonomous driving radar systems, radar-based gesture recognition for smart home applications and device control as well as in the field of medical technology for the contactless monitoring of human vital signs.
Leistungsnachweis:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS-Kreditpunkte:
1
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