Adaptive Inertia for a Virtual Synchronous Machine Using an LQR Controller Applicable to a High-Voltage DC Terminal

Marin-Hurtado, A.J.; Escobar-Mejía, Andrés; Gil-González, Walter

doi:10.1109/andescon50619.2020.9271995

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…The WTGSs are built with wind turbine generators that convert the wind movement energy into mechanical power after this power is transformed into electrical power via electrical machines. These machines can be connected directly to the electrical networks or connected through the power electronic devices [3,4]. This connection depends on the configuration type employed.…”

Section: Introductionmentioning

confidence: 99%

LQR-Based Adaptive Virtual Inertia for Grid Integration of Wind Energy Conversion System Based on Synchronverter Model

et al. 2021

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This paper proposes adaptive virtual inertia for the synchronverter model implemented in a wind turbine generator system integrated into the grid through a back-to-back converter. A linear dynamic system is developed for the proposed adaptive virtual inertia, which employs the frequency deviation and the rotor angle deviation of the synchronverter model as the state variables and the virtual inertia and frequency droop gain as the control variables. In addition, the proposed adaptive virtual inertia uses a linear quadratic regulator to ensure the optimal balance between fast frequency response and wind turbine generator system stress during disturbances. Hence, it minimizes frequency deviations with minimum effort. Several case simulations are proposed and carried out in MATLAB/Simulink software, and the results demonstrate the effectiveness and feasibility of the proposed adaptive virtual inertia synchronverter based on a linear quadratic regulator. The maximum and minimum frequency, the rate change of the frequency, and the integral of time-weighted absolute error are computed to quantify the performance of the proposed adaptive virtual inertia. These indexes are reduced by 46.61%, 52.67%, 79.41%, and 34.66%, in the worst case, when the proposed adaptive model is compared to the conventional synchronverter model.

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Section: Introductionmentioning

confidence: 99%