Three-Phase Grid-Interactive Solar PV-Battery Microgrid Control Based on Normalized Gradient Adaptive Regularization Factor Neural Filter

Abstract

In this article, a normalized gradient adaptive regularization factor neural filter based control is presented for a three-phase grid interfaced solar photovoltaic (PV)-battery energy storage microgrid system. An incremental conductance (INC) technique is utilized for the peak power extraction of a solar PV array. A battery is connected through a bidirectional converter at dc-link of voltage-source converter (VSC), and it charges and discharges as per load variation and enhances the reliability of the system. This dc–dc converter also regulates the dc-link voltage for maximum power point tracking (MPPT). This neural filter based current controller improves the dynamic behavior of the proposed system and feeds active power to the utility grid by utilizing the feed-forward term of solar PV power under variable atmospheric scenarios. A power electronics switch is used for VSC mode shifting operation between current control in the grid-connected mode and voltage control in an islanded mode to ensure continuous and adequate power to the nonlinear load. The discrete proportional and resonant (PR) controller is used for the voltage control in an islanded mode to reduce the steady-state error between sensed and reference load voltages. The voltage controller also regulates the frequency. Simulations of the microgrid system are carried out by utilizing MATLAB/Simulink software to show the effectiveness of control technique. The performance of the system is found satisfactory for various operating conditions such as load variation, load unbalancing, and solar insolation change, and validated through test results on a developed laboratory prototype.