Visible to the public Cyber-physical data stream assessment incorporating Digital Twins in future power systems

TitleCyber-physical data stream assessment incorporating Digital Twins in future power systems
Publication TypeConference Paper
Year of Publication2020
AuthorsKummerow, A., Monsalve, C., Rösch, D., Schäfer, K., Nicolai, S.
Conference Name2020 International Conference on Smart Energy Systems and Technologies (SEST)
Date PublishedSept. 2020
ISBN Number978-1-7281-4701-7
KeywordsAI based anomaly detection functionalities, anomaly detection, artificial intelligence, control functions, critical physical events, cyber-attacks, cyber-physical situational awareness, Cyber-physical systems, data stream analysis, digital twins, expert knowledge, expert systems, high-resolution PMU data, holistic data stream assessment methodology, Human Behavior, IT-OT convergence, measurement uncertainty, Monitoring, phasor measurement, phasor measurement units, Pollution measurement, power engineering computing, power grids, power system control, power system reliability, power system security, Power systems, pubcrawl, reliable grid operations, resilience, Resiliency, SCADA information, SCADA systems, Scalability, secure grid operations, security, security of data, Sparse matrices, steady system states, Steady-state, Systems architecture, transient system states

Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.

Citation Keykummerow_cyber-physical_2020