Masterarbeit – Predictive Maintenance & Environmental Monitoring In Microgenerator Swarms; M​/F​/D

fortiss is the research institute of the Free State of Bavaria for software-intensive system sand services with headquarters in Munich. Theinstitute currently employs around 150 employees,who collaborate on research, development andtransfer projects with universities and technology companies in Bavaria, Germany and Europe. Research is focused on state of the art methods, technique sand tools of software development, systems &service engineering and their application to reliable, secure cyber-physical systems, such asthe Internet of Things (IoT).

fortiss has thelegal structure of a non-profit limited liabilitycompany (GmbH). Its shareholders are the Free State of Bavaria (as majority shareholder) and the Fraunhofer Society for the Promotion of Applied Research.

To extend our research lines in our Architectures and Services for Critical Infrastructures team, we are offering a:
MSc Thesis on Graph neural networks fortopology aware predictive maintenance and environmental monitoring in emerging renewable energy infrastructures – micro-hydropower deviceswarms (m/f/d)

In recent years, the landscape of energydistribution systems has undergone a remarkabletransformation. The modernization and diversification of the energy networks has comewith severe challenges and opportunities, and theadvent of smart grids have initiated an era ofmassive data generation and exchange in thisdomain. Data from several measuring devicesdistributed across the network are now available,and this together with the power of modern machine learning techniques present opportunities for amore resilient, efficient, and sustainable energy infrastructure.

The aim of the project is to develop anautomated process for robust and accuratepredictive maintenance, diagnosis and environmental monitoring in an emerging form ofrenewable energy composed of a swarm of riverhydropower plants. To this end, learning methodsinvolving graph neural networks consideringtopological information, external knowledgesources must be researched. Our research relies onincorporating data from heterogeneous measuringdevices at different locations and incorporateelements from previous work on fault localizationin low and medium voltage energy grids.

• Review literature on graph neural networksstate of the art
• Expanding methods from previous researchon graph neural networks for fault localization inlow and medium voltage energy grids
• Scaling up and parallelizing simulation sand training e.g. via Cloud computing,Containerization and Virtualization Techniques(Cloud/Docker/Kubernetes/VMs)
• Scripting solutions to optimize data pipelines (Python/Bash)
• Integrating data from heterogeneoussensors on a 5G network
• Developing and improving Graph Neural Network Models for forecasting, diagnosis and predictive maintenance (Python/Pytorch/Matlab)
• Integrating river topology, environmentalmeasurements in propagation models
• Code Versioning and Issue Management(Git)
• Training and co-developing new technique sand ML models
• Eventually co-authoring research papersand supporting dissemination tasks.

• Student of M. Sc. in Computer Science,Electrical Engineering or similar
• Knowledge in neural networks,machine-learning, ideally on graph neural networkstoo
• Self-motivated and structured way ofworking
• Good communication skills in English
• Availability to work on-site

• International and dynamic work environment
• Flexible schedule and work in convenientlocation
• Possibility to perform research in challenging and exciting topics in the field ofmachine learning and emerging renewable energytechnologies

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