Model Free Adaptive Neural Controller for Standalone Photovoltaic Distributed Generation Systems With Disturbances

Unknown system parameters and varying disturbances are detrimental to the stability of low-inertia systems like standalone photovoltaic distributed generation systems (SPVDG). In this paper, a model-free adaptive neural controller (ANC) is proposed for the maximum power point tracking (MPPT) and grid voltage control of an SPVDG whose system model is unknown and subjected to varying disturbances. This helps in making the system more robust to sensor failures. The neural network weight update laws of the controller are derived using the Lyapunov stability criterion. It is shown that the proposed controller ensures the uniformly ultimately boundedness (UUB) of all states of the resulting closed-loop system. The performance of the proposed controller is evaluated in simulations against two other state-of-the-art controllers in the presence of disturbance and parameter intermittencies.