Student* with Master Thesis (optional) Anomaly Detection on Electrical Data on Wind Turbines

Wind Energy Systems

Student* with Master Thesis (optional) Anomaly Detection on Electrical Data on Wind Turbines

Hannover

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 800 employees work with an annual research budget of 3.0 billion euros.

Who we are …
Our primary focuses at Fraunhofer IWES are on wind energy and hydrogen technologies. Our institute is home to more than 300 scientists and employees as well as over 100 students from over 30 countries pursuing careers in applied research and development at nine sites. We secure investments in technological developments through validation, shorten innovation cycles, accelerate certification procedures, and increase planning accuracy by means of innovative measurement methods.

This team needs your support …
You will be part of the group »Technical Reliability« at our site in Hannover. At present, our team consists of eight research associates and several students. The aim of our group is to make wind turbines more reliable. We mainly investigate causes of failures and approaches to detect or prevent faults at an early stage. We analyze failure and operating data and carry out extensive field measurements. Become an active member of the team; we are keen to hear your ideas! As an international oriented IWES-team, we highly appreciate an open exchange, whether this be in German or English. Respectful cooperation is also very important to us. You are wondering what you can bring to the team?

What you will do

These duties await you …
You will be applying machine learning methods to detect anomalous behavior in electrical measurement data. You will start by preprocessing proprietary data into a universally readable format for easier analysis. Next, you will statistically describe data sets to understand normal electrical behavior patterns. Using classification algorithms and anomaly detection techniques, you will pinpoint deviations from the norm. Additionally, you will check your findings against wind turbine operational data from SCADA systems. Throughout these tasks, you will use Python, mainly work with modules like pandas, NumPy and scikit-learn. It is possible to write your master thesis in this field.

What you bring to the table

What is your background?
You are studying Electrical Engineering, Machine Learning, Computer Science, Mechanical Engineering, Environmental Engineering, Data Analytics, or a similar subject and are currently enrolled in a master’s program? Do you have a solid understanding of electrical engineering and technical concepts? Are you familiar with the basics of wind turbines as well? Great! You are proficient in Python and have a foundational understanding of machine learning. Maybe you have even worked with machine learning toolkits like TensorFlow or PyTorch yet? If you are ready to take on challenges, work independently, and participate in innovative projects, we invite you to join us.

What you can expect

What we can offer you …
We offer various opportunities to join us as a student. Whether it is an internship, where you gain a comprehensive insight into the areas of work, or the role of a student assistant, which is easy to combine with your studies. Are you looking for an exciting topic for your thesis and do you want to delve deeply into a topic scientifically? Together, we will find the right path for you! We know that studying can also be very demanding and requires a certain level of flexibility. That is no problem here, as – in agreement with your colleagues – you can decide flexibly what days and hours to work. Temporarily, you can even work remotely, depending on the job.

Eager to learn more?
If you would like to find out more information about the IWES, our research aspects, and your future colleagues, please visit our career website: https://s.fhg.de/5ei

We value and promote the diversity of our employees‘ skills and, therefore, welcome all applications – regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.

The standard contract duration is 1 year, individual agreements are possible. The working time consists of 60 hours per month. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Interested? Apply online now. We look forward to getting to know you!

If you have any further questions, please contact:
People & Development
E-mail: personal@iwes.fraunhofer.de
Phone: +49 471 14 290-230

Only online applications via the portal can be considered.
Please note that we observe the provisions of the valid General Data Protection Regulation when processing applications.

Fraunhofer Institute for Wind Energy Systems

Requisition Number: 74933 Application Deadline: