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.

This paper deals with the essentials of synchrophasor applications for future power systems aimed at increasing system reliability and resilience. In this work, several applications are presented, covering real-time disturbance detection and blackout prevention. Firstly, an advanced big-data management platform built in real-time digital simulation (RTDS) environment to support measurement data collection, processing and sharing among stakeholders is described. With this platform, a network splitting methodology to avoid cascading failures is presented and demonstrated, which upon the occurrence of a disturbance successfully isolates the affected part to avoid catastrophic cascade system outage. Online generator coherency identification is another synchrophasor application implemented on the platform, whose use is demonstrated in the context of controlled network splitting. By using synchrophasors, data-analytics techniques can also be used for identifying and classifying different disturbances in real-time with the least human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system to detect and classify critical events is outlined. Finally, the paper elaborates on the development of advanced system resilience metrics for real-time vulnerability assessment, with a focus on increasingly relevant dynamic interactions between distribution and transmission systems.

Given the increasing need to meet the new operational requirements of power systems and prepare for the future, adaptation of cutting-edge Artificial Intelligence (AI) technologies in the operational processes is paramount to timely meet the challenges. The focus of this paper is on applying AI in power system operations, in particular for the development of decision support tools. First, the paper elaborates on the decision-making process of the power system operators and presents a mirroring digital framework consisting of AI and control theory to mimic sequential decision making of the operators. Next, a demonstrating example in the field of congestion management is presented by a real-world AI use-case at TenneT TSO. The paper continues with state-of-the-art on sequential decision making applied to congestion management and elaborates on research challenges when applying AI to the power systems problems. Finally, the paper elaborates on the enabling capabilities with focus on people, data, and platform pillars an organisation needs for mastering the development as well as maintenance of AI solutions, and proposes a cyclic (agile) process approach to decrease time from development to actual deployment and cooperation between research and industry organisations.