Coordinated Power Grid Protection based on Machine Learning Methods

• Applicants: Professor Dr.-Ing. Johann Jäger; Professor Dr.-Ing. Andreas Maier
• Subject Area: Electrical Energy Systems, Power Management, Power Electronics, Electrical Machines and Drives ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535389056

The process of decarbonization leads to a massive increase in new grid devices in the electrical power grid, such as volatile feed-in systems based on regenerative energy sources, energy storage and highly dynamic loads. In addition, there are more and more unconventional network structures and grid operating modes, such as high degrees of meshing or multi-leg arrangements in the distribution grids and curative redispatch with temporary higher loadings in the transmission grids. The urgent need for coordinated grid protection to maintain supply reliability and grid security remains. Due to the increasing lack of clarity in the separation between operational and fault scenarios as well as multivariate network structures and grid operating modes, classic grid protection methods are reaching their limits. Even adaptive protection concepts cannot adequately meet this challenge in its full form. In the research project applied for, a fundamentally new grid protection approach will be taken. The basic properties of non-linear classification, competence learning and the generalization ability of machine learning methods based on neural networks should be used skillfully in grid protection technology and lead to a universally applicable and automated grid protection solution. The functional distinction between overcurrent protection, distance protection or differential protection is no longer effective here. The previous planning process of protection coordination is also becoming an automated training process for neural network structures with labeled time series of real and simulated operating and fault data. Appropriately trained agents then replace the protective devices in the field. Centralized or decentralized solutions are possible. The introduction of physical knowledge about the electrical grid into the neural network structure with the "Known Operator Learning" method ensures the important requirement of protection technology for robustness and traceability. The new approach is to be implemented and tested on a laboratory model of a power grid with digital switchgear configuration and with the help of a real-time network simulator (RTDS). With the implementation of this research project, an important contribution is made to maintaining the reliability of supply and network security in future networks and, last but not least, the digitization of the networks is promoted.

• DFG Programme: Research Grants