| The photovoltaic inverter is the core power conversion and control unit of photovoltaic power generation system,which is developing towards distributed photovoltaic generation and intelligent microgrid.Therefore,the functions of PV inverters in the future include not only grid-connected or off-grid operation,but also can achieve intelligent monitoring,power quality management,intelligent operation and maintenance,and energy interconnection.Finally,an intelligent microgrid system consisting of photovoltaic generation,peak shaving,energy storage and energy intelligent transaction is built.Taking the multifunctional photovoltaic inverter and smart micro-grid monitoring system as the research object,this paper analyzes the technology development of photovoltaic power generation and the background and significance of the project,focusing on the Control modeling,Improved MPPT algorithm,fuzzy grid-connected algorithm,four-stage energy storage control,improved PQ droop method,mode switching and intelligent monitoring of power generation based on deep learning.The main contributions of the paper are as follows:(1)An adaptive step MPPT control algorithm based on three-point judgment and a fuzzy PID deadbeat grid-connected control algorithm based on Newton/Taylor prediction is proposed.Firstly,a variable step MPPT control algorithm based on three-point judgment is analyzed,and an adaptive step MPPT fuzzy control algorithm based on perturbation and observation method is proposed.The matching problem of the maximum power point tracking speed of interleaved BOOST and the full bridge inverter power regulation speed is studied.The MPPT control scheme of solar photovoltaic panels under multi-peak effect is studied.The simulation results show that the cumulative error of the Newton interpolation algorithm is 2 times that of the Taylor interpolation algorithm,but the amount of computation is only half of the Taylor interpolation;the total harmonic distortion of the grid-connected current based on Taylor prediction is 0.54 percentage points lower than that of the deadbeat control based Newton prediction.The simulation model of the multifunctional PV inverter based on the dual-loop control is established,the experiment shows that the THD of the grid-connected current is lower than 3%under the rated power of the multifunctional PV inverter.(2)The multifunctional PV inverter 's four-stage charging control algorithm and double-loop control algorithm of off-grid operation are studied,so that the charging process of lithium battery pack is more flexible.The traditional three-stage charging control of lithium battery is analyzed,and then proposes the four-stage charging control strategy,which makes the whole charging process is smoother and prolongs the service life of the lithium battery.The experimental results show that the maximum efficiency of charging and discharging is up to 93%.(3)The dual-inverter parallel control strategy of multifunctional PV inverter based on active and reactive power increment with improved PQ droop method is proposed.The parallel control strategy of the multifunctional PV inverter is analyzed,the MATLAB simulation model of parallel control is set up,and the software realization of the parallel control algorithm is analyzed.The parallel operation of two multi-function inverters is simulated and analyzed.The results of the double parallel test show that:The output voltage of the two inverters keeps good running state,and the output bus current keeps sine wave without obvious distortion.When the two inverter modules are connected in parallel,the current imbalance is not more than 3%,which has high parallel current sharing accuracy.(4)The deep belief network predictive control technology of multi-function switching and photovoltaic power distributed instantaneous power is studied.A hierarchical control strategy based on DC bus voltage is put forward to realize switching of different modes of operation of multifunction PV inverter.The experiment proves that smooth switching can be achieved between the three working modes of off-grid,grid-connected and grid-connected energy storage.A prediction model of the instantaneous power of photovoltaic power generation based on deep belief network(DBN)is proposed,which considers the various factors that affect the power of photovoltaic power generation,and the initial input of the model is treated by the normalization of various factors.The prediction model is simulated and verified on MATLAB software.Based on Ali cloud platform,a distributed intelligent monitoring database system for photovoltaic power generation is established.Based on the B/S architecture,the PV browser monitoring software for mobile communication tools such as microcomputers and mobile phones is developed.The actual running test is carried out.The test results verify the real-time and reliability of the data transmission of the intelligent monitoring system. |
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