Two M.Sc. students specializing in Renewable Energy at the University of Agder (UiA) have successfully completed their two-month exchange in Vietnam. Working under the umbrella of the ECOWIND project, the students collaborated closely with Can Tho University (CTU), Qui Nhon University (QNU), and Central Wind Power JSC to advance their master’s thesis work.
The exchange has yielded impressive academic results and fostered strong international collaboration.
1. Advanced Fault Diagnosis in Power Inverters
Student: Jon Greger Notland
Research Topic: Fault Diagnosis of Open-Circuit Faults in Voltage-Source Inverters
Academic Team: Supervised by Prof. Khang Huynh (UiA) and co-supervised by Dr. Quoc Anh Le (CTU).
During his exchange, Jon Greger divided his time between Can Tho University and Qui Nhon University. His research has already achieved significant milestones:
- Symposium Presentation: Jon presented the initial concepts of his thesis at the Vietnam-Korea Symposium on Power Electronics held in Nha Trang, Vietnam (February 1–3, 2026).
- IEEE Conference Selection: His co-authored conference paper, “A Gray-Box Feature Selection Framework for Interpretable Open-Circuit Fault Diagnosis in Voltage Source Inverters,” has been accepted for presentation at the prestigious 24th IEEE International Conference on Industrial Informatics (INDIN 2026) in Melbourne, Australia (July 26–29, 2026).
- Co-authors: Jon Greger Notland (UiA), Khang Huynh (UiA), Quoc Anh Le (CTU), and Hoang Vu Nguyen (CTU).
- Future Outlook: The final results of his thesis are expected to generate an additional peer-reviewed publication.
2. Deep Learning for Wind Energy Maintenance
Student: Mats Gundersen
Research Topic: Predicting Wind Turbine Maintenance Events Using Deep Learning
Academic & Industry Team: Supervised by Prof. Joao Leal (UiA), in collaboration with Mr. Minh-Nhat Nguyen (Central Wind Power JSC) and Dr. Tuan-Ho (Qui Nhon University).
By bridging the gap between academia and industry, Mats collaborated directly with Central Wind Power JSC to apply cutting-edge AI to real-world renewable energy challenges.
- Key Outcomes: Mats’ thesis has produced high-quality, impactful results. Following the formal submission of his thesis, the team is actively preparing a high-level joint publication to share his findings with the wider scientific and industrial community.