Cascaded Nonlinear Control for Grid-Connected Single-Phase Inverters Utilizing Learning Compensation and Current Observer

In this paper, a cascaded control scheme based on nonlinear methods has been designed to simultaneously improve the quality of the local load voltage while also controlling the injected grid current in a grid-connected single-phase inverter system. This control approach ensures the seamless transfer between grid-connected and stand-alone operation modes without adjusting the controller structure. The proposed control structure consists of an outer current loop and inner voltage loop, each of which are motivated by separate Lyapunov based stability analysis. In an effort to reduce cost and noise sensitivity an inductor current observer is utilized. This scheme incorporates a Learning scheme to compensate for periodic disturbances which are present in the dynamic system. Moreover, since the impedance of the grid has significant effect on system stability and current control performance, parameter estimation scheme is developed to compensate for this unknown parameter. Each scheme in the cascaded system is validated through a Lyapunov stability analysis. The overall scheme is validated with an instantaneous circuit simulation where PLECS was utilized.