RESEARCH ON NONLINEAR FEATURE COMPENSATION CONTROL METHOD FOR PHOTOVOLTAIC GRID-CONNECTED INVERTERS BASED ON DATA REGRESSION

Li Cong; Zhang Qi; Liang Huan; Yang Hui; Sun Xiangdong

doi:10.19912/j.0254-0096.tynxb.2023-2153

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 1-9. DOI: 10.19912/j.0254-0096.tynxb.2023-2153

RESEARCH ON NONLINEAR FEATURE COMPENSATION CONTROL METHOD FOR PHOTOVOLTAIC GRID-CONNECTED INVERTERS BASED ON DATA REGRESSION

Li Cong¹, Zhang Qi^1,2, Liang Huan², Yang Hui¹, Sun Xiangdong¹

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Abstract

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.

Key words

photovoltaic / dead zones / grid-connected inverter / nonlinear characteristics / data-driven / online training

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Li Cong, Zhang Qi, Liang Huan, Yang Hui, Sun Xiangdong. RESEARCH ON NONLINEAR FEATURE COMPENSATION CONTROL METHOD FOR PHOTOVOLTAIC GRID-CONNECTED INVERTERS BASED ON DATA REGRESSION[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 1-9 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2153

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