Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Abstract

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of largescale power grids. This study provides an overview of the latest progress of FNET/ GridEye. The sensors, communication, and data servers are upgraded to handle ultrahigh density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication.

Refined convolution‐based measures for real‐time harmonic distortions estimation in power system dominated by inverter‐based resources

“…Real-time monitoring of harmonic distortion levels in power networks has become crucial due to the dynamic behaviour of these distortions, particularly during transient periods resulting from system-wide disturbances such as faults or changes in weather conditions. Integrating inverters-based resources into the power grid has further accentuated the need for realtime monitoring [1][2][3]. The dynamic behaviour of harmonic distortions results in destructive interference with power system equipment, leading to measurement errors and significant challenges for accurate detection and monitoring.…”