Symmetry-Guided Mechanism-Data Fusion Method for Minimum Inertia Requirement Assessment in Renewable Energy Power Systems

With the steadily increasing penetration of renewable energy, the equivalent inertia of power systems continues to decline, and frequency stability is increasingly challenged. Consequently, a rapid and accurate assessment of the minimum inertia requirement is urgently needed. To address this challenge, this study proposes a mechanism–data fusion method for minimum inertia requirement assessment in renewable energy power systems. The assessment task is decomposed into two structurally symmetric subtasks corresponding to the mechanism and data pathways, where the same system operating state serves as the common input to both pathways. In the mechanism pathway, the minimum inertia requirement is analytically calculated using a generic system frequency response model with open-loop decoupling. In the data pathway, an extreme learning machine estimates the error between the mechanism result and the time-domain simulation benchmark to correct the mechanism result. Finally, the mechanism result and the data-based error compensation are summed to obtain the final assessment. Tests on the CSEE-FS power system show that the proposed method reduces the mean absolute percentage error from 9.33% to 0.23% and the root mean square error from 0.3957 s to 0.0114 s relative to the mechanism-only method. The average computation time is 0.16 s per operating condition, meeting the requirement for rapid online assessment.