Mt optimization of Controllers of Distributed

vor 4 Wochen

Kassel, Deutschland Fraunhofer-Gesellschaft Vollzeit

**What you bring to the table**
- Background in electrical/control/power engineering or computer science
- Requirements:

- Good programming skills (e.g. Python) and willingness to improve these skills
- Preferred qualifications (one or more):

- Knowledge of power systems
- Knowledge of (heuristic) optimization algorithms
- Knowledge of control engineering (PI-controllers, signal flow diagrams, etc.)
- Familiarity with dynamic (time-domain) modeling and simulation
- Familiarity with software for dynamic modeling and simulation (e.g. Simulink, Modelica, PowerFactory)

**What you can expect**

In modern electric power systems, there is a paradigm shift from large central power plants to decentralised and renewable power generators and battery storage. The numerous smaller units increase the complexity of power systems. In particular, the dynamic stability (in the time-frame of milliseconds to seconds) of such systems is a growing concern, as such converter-dominated systems have lower rotational inertia due to the absence of synchronous machines.
The dynamic behaviour of power electronic converters is determined by their controllers. In recent years, controllers for converters have been introduced that contribute to the stability of power systems (e.g. grid-forming control). The complexity of these controllers and their interaction make it difficult to adjust them manually by hand. The aim of this research thesis is to implement heuristic optimisation methods (a form of artificial intelligence) to automatically adjust the parameters of such converter controllers to achieve the desired behaviour and improve the stability of power systems.

**The main tasks include**:

- Improve your ability to write clean code and develop software as part of a team (version control, git, documentation, unit tests, etc.).
- Obtain understanding of heuristic optimization methods (e.g. particle swarm optimization, genetic algorithm).
- Document and publish (thesis and at a conference if results are good) your results so that others can built on them.

The position is initially limited to 6 months. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

If you have any questions about this position, please contact:
Dr.Ing. Simon Eberlein

Fraunhofer Institute for Energy Economics and Energy System Technology IEE

Requisition Number: 61720 Application Deadline: 01/31/2023

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