Robust Load-Model Parameter Identification from Ambient Measurements Under Time-Varying Nominal Power

As inverter-based renewable generation increases, load modeling from ambient measurements is important for power-system stability assessment. Under ambient conditions, voltage and frequency variations are small, whereas time-varying nominal power can substantially affect load-power variation. If nominal-power variation is not considered, it may be misattributed to voltage- and frequency-dependent load responses, resulting in biased parameter estimates. Because this misattribution depends on the variations within each time window, single-window estimation may suffer reduced accuracy. This study proposes a method that models time-varying nominal power using an autoregressive moving-average model to separate nominal-power variation from voltage- and frequency-dependent load responses and aggregates validation errors across multiple time windows to reduce dependence on any specific window. Numerical simulations under two nominal-power variation levels were compared with single-window estimation and a previously proposed sensitivity-based window-selection method. When the maximum 100 s peak-to-peak nominal-power variation was 1.0% of the mean nominal power, the proposed method achieved an overall error of 0.100, 86% lower than the mean single-window error and 79% lower than the best sensitivity-based result. Additional analyses under modified identification and data-generation conditions showed that the proposed method generally maintained comparatively low identification errors, although accuracy varied with the conditions.