PhD on Harmonic Losses in Power Transformers at Universiteit Twente
7522 Enschede, Overijssel, Netherlands -
Full Time


Start Date

Immediate

Expiry Date

28 May, 25

Salary

2.901

Posted On

28 Jan, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Electrical/Electronic Manufacturing

Description

KEY TAKEAWAYS


  • Hours
    40 hr.

  • Salary indication
    Salary gross/monthly
    based on full-time
    € 2,901 - € 3,707

  • Deadline
    14 Mar 2025
    The Power Electronics Group within the Department of Electrical Engineering has a vacancy for a PhD researcher (fully funded, 4 years) to work in the European Partnership on Metrology joint research project ENSURE - Metrology for Electric energy and supply reliability. The focus of the ENSURE project is to perform the metrology research necessary to support the reliability of our electricity grids.
    Power transformers and other grid components are suffering from the lower grid power quality caused by renewable generation. In strong collaboration with utilities and power transformer manufacturers, you will research the impact of grid harmonics on the losses of power transformers. Through your research, new insights in harmonic losses of power transformers should be achieved, that will support manufacturers to design and build the next generation of low-loss power transformers.
    The goal of the PhD position is to model, measure and analyze the impact of harmonic losses in power transformers. Your R&D work involves the development of a frequency-dependent analytical model for harmonic losses in power transformers with different core magnetic materials (CRGO steel, amorphous metal core, etc.) and different winding configurations. This model should consider the total losses including the copper (load loss) and core (no-load loss) losses. To verify and improve the modelling, you will build lab-scale transformers and perform extensive systematic experiments on the total harmonic loss of the constructed transformers. ML/AI tools such as KANN (knowledge-aware artificial neural networks) will be used for model improvement as well as for proposing scaling laws for real power transformers.
Responsibilities

Please refer the Job description for details

Loading...