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) Publisher 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) Publisher 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) Publisher 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) Publisher 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)
This part of the course focuses on how to utilize ML methods to model and operate electric power systems. Electric power systems consist of generation units such as PV, loads or consumers and the grid that connects those actors and supports to transport energy. This part of the course helps to understand the data-driven modelling of generation units (e.g. PV & fuel cells), modelling of load behavior, and to formulate and solve a state estimation problem for distribution grids using neural networks.
This part of the course includes lectures to introduce the basics that are followed by practical examples and coding.
Performance accreditation:
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>