Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms

Abstract

This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.