Single-phase Grid-connected PV System with Golden Section Search-based MPPT Algorithm

Maximum power point tracking(MPPT)is a technique employed for with variable-power sources,such as solar,wind,and ocean,to maximize energy extraction under all conditions.The commonly used perturb and observe(P&O)and incremental conductance(INC)methods have advantages such as ease of implementation,but they also have the challenge of selecting the most optimized perturbation step or increment size while considering the trade-off between convergence time and oscillation.To address these issues,an MPPT solution for grid-connected photovoltaic(PV)systems is proposed that combines the golden section search(GSS),P&O,and INC methods to simultaneously achieve faster convergence and smaller oscillation,converging to the MPP by repeatedly narrowing the width of the interval at the rate of the golden ratio.The proposed MPPT technique was applied to a PV system consisting of a PV array,boost chopper,and inverter.Simulation and experimental results verify the feasibility and effectiveness of the proposed MPPT technique,by which the system is able to locate the MPP in 36 ms and regain a drifting MPP in approximately 30 ms under transient performance.The overall MPPT efficiency is 98.99%.

Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach

This paper proposes a new hybrid maximum power point tracking(MPPT)control strategy for grid-connected solar systems based on Incremental conductance—Particle Swarm Optimization and Model Predictive Controller(IncCond-PSOMPC).The purpose of the suggested method is to create as much power as feasible from a PV system during environmental changes,then transfer it to the power grid.To accomplish this,a hybrid combination of incremental conductance(IncCond)and particle swarm optimization(PSO)is proposed to locate maximum power,followed by model predictive control(MPC)to track maximum power and control the boost converter to achieve high performance regardless of parameter variations.A two-level inverter,likewise,controlled by Model Predictive Control,is employed to inject the PV power generated.In this application,the MPC is based on minimizing the difference between the reference and prediction powers,which is computed to select the switching state of the inverter.The proposed system is simulated and evaluated in a variety of dynamic conditions using Matlab/Simulink.Results reveal that the proposed control mechanism is effective at tracking the maximum power point(MPP)with fewer power oscillations.