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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. 

Real-time data is gaining more importance in engineering. With archived data and the real-time data, utilities are making systems more robust by developing new methods for controlling and monitoring. Frequency information of the system is one of the key factors for better controlling and monitoring purpose. So acquiring real-time frequency information at a low cost is really important. Raspberry Pi based frequency data acquisition is one of the solutions to get data remotely. This paper discusses two main topics: (i) The complete procedure for acquiring the real-time data using Raspberry Pi, Multi-chip package 3008 and LabVIEW. (ii) Modeling of Enhanced Phase Locked Loop (PLL) and its integration with the data acquisition system for processing the real-time data to extract the frequency and amplitude information.