Research Of Model Predictive Control For Photovoltaic Quasi-Z-source Inverter

In the past century,with the rapid development of the industrial revolution,human beings have consumed a large number of non-renewable energy mainly coal and oil,which not only caused the shortage of non-renewable energy on the earth,but also caused a series of environmental pollution problems.As a kind of renewable energy,solar energy has advantages of abundant reserves,environmental protection and universal.Solar energy can not only be used for light and heat,more importantly it can be used for electricity generation,which could provide a steady stream of electricity for human production activities.Therefore,the photovoltaic power generation system has attracted the attention of experts from domestic and overseas,and the inverter as a core component has become the research focus in modern society.As a new type of inverter topology,Z-source inverter(ZSI)effectively solves the problems of poor reliability,low efficiency and high output waveform distortion in traditional inverters.In addition,quasi-Z-source inverter(q ZSI)is a novel topology of Z-source inverter.It not only has the advantages of Z-source inverter,but also can make the input current continuous.Therefore,this topology has a very strong development potential in the field of photovoltaic power generation and has been applied in many areas.Model predictive control(MPC)has been widely used in power electronic converter control due to its excellent dynamic performance,convenience and ease of multi-objective optimization.Therefore,this paper studies the model predictive control strategy based on photovoltaic quasi-Z-source inverters.Firstly,the limitations of the traditional inverter are analyzed,the quasi-Z-source inverter is introduced,and the circuit topology,modulation mode and traditional closed-loop control strategy of the quasi-Z-source inverter are introduced.Then,taking the classical quasi-Z-source inverter as the research object,the working principle of it in the non-shoot-through and shoot-through states are analyzed,and the discrete mathematical model is established to provide a theoretical basis for the subsequent design of model predictive control strategy.The sequential-model predictive control(S-MPC)strategy is adopted to solve the problem of complex weighting factors adjustment in the finite control set-model predictive control(FCS-MPC)strategy of quasi-Z-source inverter.According to the priority of the controlled object,S-MPC strategy calculates the corresponding cost functions in turn,so as to eliminate the weighting factors and simplify the implementation of the control method.The simulation and experimental results verify that the proposed S-MPC strategy has the same good control effect in terms of inductance current,capacitance voltage and output current,and has excellent dynamic performance compared with FCS-MPC.To solve the problem of S-MPC control performance degradation caused by the perturbation of load electrical parameters,an adaptive sequential-model predictive control(AS-MPC)strategy was proposed.AS-MPC strategy takes the perturbation of load electrical parameters as the total disturbance of the system,and uses the parameter adaptive method to estimate and update the control law,thus improving the robustness of the system to the perturbation of load parameters.The simulation and experimental results show that the proposed AS-MPC strategy has the characteristics of no weighting factors,simple structure,excellent control performance,fast dynamic response and strong robustness to parameter perturbation.For the photovoltaic quasi-Z-source inverter system based on adaptive sequential-model predictive control,the maximum power point tracking(MPPT)was realized by the disturbance observation method(P&O),and the system simulation model was built in Matlab/Simulink.Finally,the principled sample machine of the quasi-Z-source inverter based on DSP28335 with 60 W power is designed.The selection criterion of key components of the quasi-Z-source inverter is given.The experimental comparisons of different model predictive control strategies are made in the principled sample machine.