Research On MPPT Technology Of Photovoltaic Grid-connected Power Generation System

Nowadays,with the rapid development of economy,the problem of energy scarcity and environmental deterioration is becoming more and more serious.It is urgent for us to search for new pollution-free and new energy sources.Solar energy has attracted people's attention because of its ubiquitous,non-exhausting,non-polluting characteristics.The most typical way to make use of solar energy is photovoltaic power generation,and as the control is more intelligent,it also promotes its wide application.The maximum power point tracking(Maximum Power Point Tracking,MPPT)technology plays an important position in the whole the solar power system,its optimization directly affects the output efficiency of the whole system.In this thesis,the following research is carried out on the MPPT technology of photovoltaic grid connected power generation system.First of all,aiming at the contradiction between the tracking accuracy and the tracking speed caused by the fixed step length of the traditional disturbance observation method,an MPPT algorithm based on variable-duty-cycle disturbance step size under uniform illumination is proposed.The perturbation step length of the algorithm is added to the variation of the slope of the P-U curve of the PV array,and the step length is compensated when the illumination abrupt change is large,and the maximum output power of the PV array is tracked by constantly changing the duty cycle of the Boost circuit power switch.Simulations show that compared with the traditional fixed-step perturbation observation method,the algorithm effectively reduces the unbalance between the optimization accuracy and the speed.Secondly,aiming at the problem of multi-peak output when the PV array is partially shaded,a hybrid algorithm based on particle swarm optimization combined with perturbation observation method is proposed to perform the MPPT process.Based on the modified parameters of the particle swarm algorithm,the particle velocity and position update equations are constructed,and the iterative comparison function is used as a criterion to filter out ineffective particles,until the completion of the global fast tracking,and using variable step perturbation observation method to do local optimization.The maximum power under the environmental conditions is obtained based on the power threshold.The simulation results show that the algorithm effectively solves the problem that the single particle swarm algorithm is trapped in the local extremum,and effectively improves the output efficiency of the photovoltaic system in a complex environment.