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While dq admittance models have shown to be very useful for stability analysis, extracting admittance models of inverter-based resources (IBRs) from the electromagnetic transient (EMT) simulation environment using frequency scans takes time. In this letter, a new perturbation method based on Gaussian pulses in combination with the system identification algorithms shows great promise for parametric dq admittance model extraction. We present the dq admittance model extracting method for a type-4 wind turbine. Challenges in implementing Gaussian pulse excitation are also pointed out. The extracted dq admittance model via the new method shows to have a high matching degree with the measurements obtained from frequency scans. 

Electromagnetic transient simulation of parallel-connected 4-MW type-3 wind turbines based on original equipment manufacturer's real-code turbine model shows 1.2-Hz turbine–turbine oscillations in reactive power. This letter reveals why such oscillations occur in the individual var measurement while being insignificant in the total var measurement, regardless of the varying grid impedance. We adopt two analysis approaches, i.e., open-loop single-input single-output analysis and network decomposition. These two approaches differ in their treatment of turbine–network interaction. The open-loop analysis shows that the turbine–turbine oscillation mode is due to an open-loop system pole being attracted to an open-loop system zero. Furthermore, we use a network decomposition method to explain why this mode is observable in individual vars, while not observable in the total var. The entire system of n turbines can be viewed as n decoupled circuits. For the two-turbine case, the system has an aggregated mode and a turbine–turbine oscillation mode. The aggregated mode is associated with a circuit associated with the total var, while the turbine–turbine oscillation mode is associated with the var difference and is insensitive to the grid parameters. 

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.