Master Thesis

vor 3 Monaten


Erlangen, Deutschland Siemens Energy Vollzeit

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  Dynamic reduction of electric transmission networks in DIgSILENT PowerFactory Implementing and testing of dynamic model reduction algorithms (mostly in Python) Design of improvements to the model reduction techniques using power system analysis and algorithmic approaches Evaluation and presentation of project results What You Bring Currently Master study in electrical engineering, power engineering or a comparable subject at a university or college Good academic record and relevant knowledge and interest in electrical power systems and its control systems Experience with or can quickly learn power system simulation tools, such as DIgSILENT PowerFactory Good knowledge of power system dynamics and control theory  Familiarity with Python Familiarity with algorithms, data structures, optimisation techniques is desirable Work well independently or in a team Fluent English skills

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