Artificial Intelligence and Advanced Data Analytics for Power Electronics

Welcome to Artificial Intelligence and Advanced Data Analytics for Power Electronics

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.

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
  
  

Learning objectives: By the end of the course, students will be able to:
1. Explain the fundamental concepts of artificial intelligence and advanced data analytics relevant to power electronics.
2. Apply AI and data-driven methods to model, analyze, and control power electronic systems across design, operation, and maintenance stages.
3. Implement selected AI techniques in Python/Matlab for real-world case studies, including digital twin and physics-informed approaches.
4. Critically evaluate the performance, limitations, and applicability of AI-based solutions in power electronics applications.
5. Integrate theoretical knowledge with practical skills to produce a complete project report demonstrating problem-solving competence.

Teaching methods: Lectures, Small assignments, Excercise

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.

Criteria for assessment: Student performance will be assessed based on the following criteria: 

1. Technical accuracy and depth – Correct application of AI and advanced data analytics methods to the assigned power electronics problems, with clear justification of the chosen approach. 

2. Practical implementation skills – Ability to implement the discussed methods in Python/Matlab, including correct use of tools, data processing, and model validation. 

3. Critical analysis – Demonstration of understanding in evaluating results, discussing limitations, and suggesting improvements or alternative solutions. 

4. Quality of the final report – Clarity, structure, completeness, and adherence to academic standards in the written report. 

5. Engagement and collaboration – Active participation in discussions, teamwork in the project, and constructive contributions during the hands-on sessions. The final grade will be determined based on the project report (main component) and the student’s active participation throughout the course. 

Remarks: Workload distribution (28 hours of work per ECTS, total 3 ECTS = 84 hours):

In total about 84 hours (18 hours teaching, 12 hours practicing, 24 hours preparation, 20 hours finalizing the student report, and 10 hours examination). Theteaching and practicing hours correspond to the three-day on-site lectures and hands-on sessions. Preparation includes pre-course reading of the assignedliterature, software setup, and review of prerequisite concepts. The finalizing of the student report covers data analysis, documentation, and refinement ofproject results. Examination refers to the preparation and submission of the final report, including potential revisions based on feedback.

Key literature: TBA

Organizer:

• Assistant Professor Shuai Zhao
• Professor Huai Wang

Lecturers:

• Professor Huai Wang AAU Energi, 
• Assistant Professor Shuai Zhao AAU Energi,
• Assistant Professor Mateja Novak AAU Energi, 
• Associate Professor Subham Sahoo AAU Energi

ECTS: 3

Date: 22, 23, 24 april 2026

Place: Aalborg University, AAU Energy, and Online (Hybrid) Pontoppidanstraede 105, 4.127, 9220, Aalborg, Denmark

City: Aalborg

Number of seats: 30

Deadline: 31 March 2026

Course fee: PhD Students (outside Denmark): 6000 DKK and other participants (from Industry and Universities): 8000 DKK (excluding VAT). Guests at AAU Energy can also attend the course for free.

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.