Qiang shares his topic regarding using deep learning methods to do cycloidal marine propeller diagnostics, “It’s about building a system integrated with AI models that can be trained offline with the historical data to detect when the propeller is not working properly online. I’ve done some paper review work and tried to find the appropriate methods to tackle this problem – and have already tested some of the models with a huge amount of historical data, and we are still iterating the models. This is pure data science work in a specific industrial issue, and I am excited to see the result in the end! Because although it’s about designing a system for the propellor’s diagnostic, the methods are also adaptable for the grid in general, as anomaly detection will play an increasingly important role in improving the stability and safety of future power grids”.

Joining three other EIT InnoEnergy students doing their thesis at this ABB location (they are working on other cool topics related to renewable energy or energy storage technology), Qiang shares what internship life is like: “It’s full of the unknown, challenging and enriching! Our colleagues are nice and helpful, and because ABB CRC is a research centre, they have great facilities to support your thesis research. For example, I use a strong PC with high computation capability just for my thesis work. And ABB offers thesis students free accommodation and pays a good salary. The most valuable thing is the R&D working experience as I’ve never done an R&D job before. It allows me to learn how the R&D teams work and how they incorporate with the market or product team. This gives me a reference for choosing my future career.”

When asked why he chose the MSc in Smart Electrical Networks and Systems programme, he explained, “After working one year in the solar industry, I started to think about how much renewable energy can be integrated into the power grid. Since the energy transition is like a systemic change, I want to see more from the upstream and know more about the technologies that might help this revolution. The programme aligns well with the field of electrical engineering, particularly in smart grids and renewable energy integration. And the focus on developing technical and entrepreneurial skills is also important in today’s rapidly changing technological landscape. Furthermore, the emphasis on data-driven applications reflects the increasing role that data analytics and machine learning play in electrical engineering. This programme offers a comprehensive and forward-thinking approach to electrical engineering education. I’ve been given many options to explore topics I like.”

“The thesis presented me with a specific industrial problem that needs to be solved using deep learning algorithms. However, since my major is not machine learning, exploring solutions has given me new and relevant knowledge to propose viable solutions,” says Qiang. “Because I worked as a market analyst before, I am a data-driven person who likes to use numbers to tell stories – my time in the programme has helped me discover that I now want to work as a Data Analyst in the renewable energy ecosystem. EIT InnoEnergy has been very helpful along the way and not just from an education angle – also to help me find my path with insightful tools such as the GC Index or to give me the skills to apply at ABB with useful tips and coaching from their career services. It’s been an enriching journey, and I can’t wait to see where it leads next!”

 

Find out where EIT InnoEnergy Master School could lead you! Browse our innovative programmes here.