Control strategy of T-type three level dimming power supply based on RBF PI algorithms
DOI：10.19783/j.cnki.pspc.191440
Key Words:dimming power supply  three-level T-type inverter  dynamic characteristics  harmonic  RBF neural network
Author NameAffiliation
SUN Zhang School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China 
WU Fan School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China 
PU Xu School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China 
WU Xunbing School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China 
WEI Zhongjun School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China 
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Abstract:In order to improve the dynamic performance and suppress the harmonic of a single-phase three-level T-type inverter in navigation dimming power supply, a new method is presented based on the Radial Basis Function (RBF) neural networks combined with the gradient descent method. First, the mathematical model and analysis of three-level T-type inverter topology is established. The dynamic characteristics of the system are researched in detail using the root track method, and the key parameters which influence the output responses are revealed. On this basis, a new control technique is proposed, one which uses RBF neural networks to identify the Jacobian information, and dynamically adjust the control parameters by the gradient descent method. Finally, a comparative simulation analysis is carried out for level dimming, open circuit load, short circuit load based on MATLAB/Simulink, and experiments are carried out based on the experimental platform of a 30 kW dimming power supply. The results show that the proposed method can effectively improve dynamic performance and output current harmonic. This work is supported by Major Equipment Development Project of Sichuan Provincial Economic and Information Committee (No. 2018CD00113) and the Sichuan Provincial Science and Technology Department (No. 2019JDKP0049).
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