Master Thesis - Dynamic Power System Model Reduction in DIgSILENT PowerFactory
About the Role
Location Germany Bayern Erlangen Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy Global GmbH & Co. KG Organization Grid Technologies Business Unit Digital Grid Full / Part time Full-time Experience Level Student (Not Yet Graduated) Location: ERL S SP 11 Mode of Employment: Part-time / Fixed Term How You’ll Make an Impact
1. Dynamic reduction of electric transmission networks in DIgSILENT PowerFactory
2. Implementing and testing of dynamic model reduction algorithms (mostly in Python)
3. Design of improvements to the model reduction techniques using power system analysis and algorithmic approaches
4. Evaluation and presentation of project results
What You Bring
5. Currently Master study in electrical engineering, power engineering or a comparable subject at a university or college
6. Good academic record and relevant knowledge and interest in electrical power systems and its control systems
7. Experience with or can quickly learn power system simulation tools, such as DIgSILENT PowerFactory
8. Good knowledge of power system dynamics and control theory
9. Familiarity with Python
10. Familiarity with algorithms, data structures, optimisation techniques is desirable
11. Work well independently or in a team
12. Fluent English skills