Adaptive virtual inertia emulation based on policy gradient clipping for low-inertia microgrids with phase-locked loop dynamics

Abstract

The high-penetration of sustainable energy resources in the hybrid microgrids necessitates deploying the power electronic interface systems (e.g., rectifiers, inverters, and converters) for conversion purposes. However, the utilization of such technologies reduces the inertia of microgrids which highly threaten their stability. The stability challenges of microgrids are heightened when the phase-locked loop devices are installed in the converter-based systems. In this work, a fractional order disturbance-observer-based control (FO-DOBC) is developed for advanced virtual inertia control (AVIC) of microgrids with sustainable resources, electric vehicles, and storage units. In particular, the effect of phase-locked loop’s dynamics on the stability of microgrid is investigated. To dynamically respond to the disturbances in the microgrid and phase-locked loop’s dynamics, the coefficients embedded in the FO-DOBC are adaptively adjusted by the stochastic policy gradient clipping. By training the neural networks of SPGC, the FO-DOBC controller is designed in such a way that maximizes a reward function defined based on the system requirements. The comprehensive examinations based on the Arduino testbed are carried out to appraise the feasibility of the suggested virtual-based controller in a real-time framework. The real-time outcomes of the microgrid reveal that the AVIC based on FO-DOBC controller (designed by the stochastic policy gradient clipping) provides better responses than conventional virtual inertia control. Moreover, the suggested AVIC controller provides a higher level of stability against the reduction of inertia (between 1 % to 10 %) from its nominal value.