Description

Artificial intelligence (AI) has significantly revolutionized research activities and industrial applications in image processing and natural language processing. Likewise, the synergy of power electronics and computer science is expected to unleash great potentials in power electronic systems as well with their transition towards data-rich ones. From the power electronics perspective, this course aims to focus on two essential aspects of this interdisciplinary field, i.e., artificial intelligence and advanced data analytics. It is organized following a typical pipeline when implementing data-driven solutions in power electronics, ranging from the initial data collection to the final decision-makings. As a 3-day course, it includes fundamentals, tools, applications, hands-on exercises, and outlook, which are specifically tailored for power electronic applications. Combining with several case studies where AI has shown great benefits, the attendees are expected to establish solid foundations and skills of AI and data analytics to address core challenges in data- driven applications in power electronics.

Find the detailed course program on PhD Moodle: phd.moodle.aau.dk/course/view.php?id=2172 

Price

8000 DKK for the Industry and 6000 DKK for PhD students outside of Denmark (VAT-FREE Education)

The Danish universities have entered into an agreement that allows PhD students at a Danish university (except Copenhagen Business School) the opportunity to free of charge take a subject-specific course at another Danish university.
Read more here: https://phdcourses.dk/           

Questions

hr@energy.aau.dk   

More information

https://www.energy.aau.dk/research/phd   

Prerequisites

• Fundamentals of power electronics
• Fundamentals of probabilistic models and statistical analysis
• Experience with MATLAB/Python

*  Please get familiar with Python basics and set up your Google Colab account before the course. A tutorial of Google Colab can be found: https://www.tutorialspoint.com/google_colab/google_colab_tutorial.pdf 

*  Matlab installed with the predictive maintenance toolbox. You may find more details in the below link: https://www.mathworks.com/products/predictive-maintenance.html 

Form of evaluation

The course is accompanied by a hands-on team project so that the theoretical tools introduced in the course can be implemented in real applications. The course evaluation will be based on the project report.

Course literature       

• [1]   S. Zhao, Y. Peng, Y. Zhang and H. Wang, "Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning," in IEEE Transactions on Power Electronics, vol. 37, no. 10, pp. 11567- 11578, Oct. 2022, doi: 10.1109/TPEL.2022.3176468.
• [2]  Y. Peng, S. Zhao and H. Wang, "A Digital Twin Based Estimation Method for Health Indicators of DC–DC Converters," in IEEE Transactions on Power Electronics, vol. 36, no. 2, pp. 2105-2118, Feb. 2021, doi: 10.1109/TPEL.2020.3009600.
• [3]  M. Novak and T. Dragicevic, "Supervised Imitation Learning of Finite-Set Model Predictive Control Systems for Power Electronics," in IEEE Transactions on Industrial Electronics, vol. 68, no. 2, pp. 1717-1723, Feb. 2021, doi: 10.1109/TIE.2020.2969116.
• [4]  V. S. B. Kurukuru, M. A. Khan and S. Sahoo, "Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach," in IEEE Transactions on Power Electronics, vol. 37, no. 11, pp. 12938-12943, Nov. 2022, doi: 10.1109/TPEL.2022.3180943.
• [5]    S. Zhao, F. Blaabjerg and H. Wang, "An Overview of Artificial Intelligence Applications for Power Electronics," in IEEE Transactions on Power Electronics, vol. 36, no. 4, pp. 4633-4658, April 2021, doi: 10.1109/TPEL.2020.3024914.