An Adaptive Optimal Virtual Inertia Control Scheme for Stability Enhancement of Utility-Scale PV Power Plants

Utility-scale inverter interfaced photovoltaic (PV) power plants integrated to weak grids experience several challenging issues, viz., poorly damped oscillations of power, transient dc bus over/under voltage and instability during LVRT, which ultimately result in tripping of units. To overcome these challenges, a reinforcement learning (RL) based adaptive optimal virtual inertia control (AOVIC) scheme has been proposed in this paper. The implemented virtual inertia control (VIC) scheme consists of two parts, (1) the virtual governor and (2) virtual inertia, which have been emulated using the stored energy of the DC-link capacitor. Small signal modelling and stability analysis have also been performed to demonstrate the effectiveness of such a VIC scheme for weak grid-tied PV power plant under variations in grid strength. Thereafter, an adaptive dynamic programming (ADP) strategy has been utilized for implementing the RL-based AOVIC scheme, which optimally tunes the VIC loop in a model-free manner with an aim of enhancing the power oscillation damping. Finally, the efficacy of the proposed AOVIC scheme has been evaluated by performing numerical simulations on a detailed nonlinear model of a weak grid-tied PV power plant.