THREE PHASE GRID INTERACTIVE SOLAR PV- BATTERY MICROGRID CONTROL BASED ON NORMALIZED GRADIENT ADAPTIVE REGULARIZATION FACTOR NEYRAL FILTER

Abstract

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 proposed system and feeds active power to the utility grid by utilizing the feed forward term of solar PV power (FFTPV) 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 controller (PR) 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 microgrid system are carried out by utilizing MATLAB / Simulink software, to show the effectiveness of control technique. The performance of system is found satisfactory for various operating conditions like load variation, load unbalancing and solar insolation change and validated through test results on a developed laboratory prototype.

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