RESEARCH ON NONLINEAR FEATURE COMPENSATION CONTROL METHOD FOR PHOTOVOLTAIC GRID-CONNECTED INVERTERS BASED ON DATA REGRESSION
Addressing the impact of nonlinear characteristics such as dead-zones on the power quality of photovoltaic grid-connected inverters,this paper combines data-driven compensation methods with traditional control to investigate a dynamic and static characteristic optimization approach for grid-connected inverters.Firstly,a repetitive controller is utilized as the basis for online data training,elucidating the mechanism and validity of the data source.Secondly,an approximate linear regression method is employed to obtain a data model,reducing the dependence on storage space for data-driven methods,ensuring necessary compensation bandwidth,and solving the feasibility of data application.This model is then applied to the compensation loop of a traditional low-order controller,enabling the system to achieve precise control with sufficient stability margin.Data correlation analysis and experimental results demonstrate the feasibility and effectiveness of this compensation method.
photovoltaicdead zonesgrid-connected inverternonlinear characteristicsdata-drivenonline training
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