Data-Driven online framework for power system strength assessment

As the transition to a renewable-dominated grid progresses, traditional offline system strength assessment methods, such as the offline short-circuit ratio (SCR), become inadequate for accurately assessing system strength in near-real time. This study proposes a novel online data-driven framework utilizing phasor measurement unit (PMU) data to estimate SCR in near-real time without requiring detailed grid models or parameters. A deep neural network (DNN) is employed to learn the correlation between the collected PMU data and SCR values across different grid nodes, allowing for efficient and accurate online system strength assessment. The proposed algorithm demonstrates versatility and applicability across various power grids and operating conditions, providing a significant advancement in the near-real time online evaluation of system strength. Across two systems, the estimator achieves testing mean square error (MSE) of 0.142 (IEEE–39) and 0.0191 (simplified Australian 14-generator grid), executes in 1.2–1.4 ms per window on a CPU, and remains robust to 5 % noise and 20 % data gaps (MSE ≤ 0.171/0.184 and 0.0289/0.0315, respectively. This work not only fills existing significant research gaps but also paves the way for improved system monitoring in modern power grids amidst the growing integration of renewable energy technologies.