Power Systems Intern: Wind & PSS®E Dynamics

Siemens Mobility; Princeton (NJ); On-site; USD 48,000 - 65,000; Full time

Job summary

Siemens Mobility in Princeton, NJ invites an internship focused on power system dynamics, PSS/E modeling, and renewable integration. You will implement and validate PSS/E UDMs for inverter control, build wind energy models, and develop Python tools for automation and analysis.

The role emphasizes hands-on simulation work, collaboration with the Autonomous Controls and Operations Research group, and on-site participation in a fast-paced research setting.

Qualifications

• Enrollment in an MS or PhD program in Electrical Engineering or Power Systems.
• Hands-on experience with PSS/E dynamic simulations and model execution.
• Familiarity with Fortran and Python for engineering workflows.

Responsibilities

• Develop, implement, compile, integrate, and validate PSS/E UDMs for inverter control and renewable models.
• Model wind energy conversion systems and wind plants for stability studies.
• Use Fortran-based workflows and compilers to build dynamic model libraries.
• Perform dynamic simulations of inverter-based resources and test GFM/GFL behavior.
• Develop Python tools for automation, batch runs, data analysis, and visualization.
• Support model benchmarking, validation, and comparisons with references.
• Prepare technical docs covering assumptions, methods, and findings.

Skills

PSS/E dynamics

Fortran programming

Python scripting

Wind energy modeling

GFM/Grid-Following concepts

Grid-Forming concepts

English proficiency

Education

MS/PhD in Electrical Engineering or Power Systems

Tools

PSS/E

Fortran toolchains

Python (NumPy, Pandas, Matplotlib)

Job description

Siemens Mobility in Princeton, NJ invites an internship focused on power system dynamics, PSS/E modeling, and renewable integration. You will implement and validate PSS/E UDMs for inverter control, build wind energy models, and develop Python tools for automation and analysis.

The role emphasizes hands-on simulation work, collaboration with the Autonomous Controls and Operations Research group, and on-site participation in a fast-paced research setting.