Internship: Master Thesis - Power Converter Harmonics Reduction Based on Deep Learning Algorithms

Location: Wandre, Belgium
Duration: minimum 3 months with the possibility of extension
Start Date: To be determined with the student
Application Deadline: 30/09 

Overview  

CE+T Power is seeking a Master’s level student for a thesis internship focusing on the application of deep learning algorithms for the reduction of harmonics in power converters. This project aims to enhance the power quality of static converters in the context of increased renewable energy integration into the power grid. 

Thesis Description  

In the context of renewable energy penetration as production source, high power centralized electromechanical generators are progressively replaced by distributed static converters. Power quality of these converters therefore becomes a concern of growing importance. A significant part of power quality is related to harmonic generation and damping inside the static converter. A lot of theories exist to improve power quality of AC grid connected converters. 

The level of complexity of these algorithms varies from simple control loops to complex self-tuning systems. From an industrial perspective, developing and improving these algorithms is a very difficult task. On the one hand, the grid connection requirements become always more stringent. On the other hand, grid parameters can change in a very large range depending on the particular installation location. 

The proposed master thesis will consist in investigating the applicability of stateof-the-art deep learning algorithms to achieve better static converters power quality and robustness to parameter variations than deterministic approaches. Such learning algorithms appear promising given the possibility to ingest data acquired over one or several grid periods and leverage them over the next period. They may also be applicable to DC micro-grids, a possible extension of the thesis. 

After a short review of existing current control and harmonic reduction algorithms, the student will analyze possible algorithms that could be applied to this specific task. They will carry out simulations to validate first concepts and evaluate hardware resources required to perform real time calculations.  

They will choose – and justify the choice of – relevant test environments among the numerous ones available at CE+T Power, e.g., Hardware-In-the-Loop (HIL), FPGA-based control boards, etc. This master thesis lies at the intersection of the control, power electronics and deep learning algorithms fields, a challenging combination. It constitutes a great opportunity to advance knowledge in areas of major importance for the ongoing energy transition. 

Candidate Profile  

• Currently enrolled in a Master’s program with a focus on electrical engineering, computer science, machine learning, or a related field. 
• Keen interest in the application of machine learning techniques to power electronics. 
• Ability to conduct comprehensive research and apply theoretical concepts in practical settings. 
• An internship agreement is not mandatory, offering flexibility in terms of academic collaboration.  

Offer 

• Opportunity to work on pioneering research with direct applications in improving renewavle energy systems. 
• Work alongside experts at CE+T Power with the possibility to conduct part of the thesis at Texas A&M University  
• Contribute to the development of innovative solutions that enhance energy efficiency and sustainability. 
• A dynamic place of work, where interns are integral team members 

How to Apply  

Interested candidates should submit a CV and a cover letter outlining their interest and qualifications for the thesis project.  

Applications and questions should be sent to f.frebel@cet-power.com