Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (2): 142-150.doi: 10.11985/2022.02.016

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A Data-driven Model of Peak Current Control Boost Converter Based on Neural Network Method

WANG Runnan(), XIE Fan(), ZHANG Bo()   

  1. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641
  • Received:2021-11-10 Revised:2022-02-13 Online:2022-06-25 Published:2022-08-08
  • Contact: XIE Fan E-mail:365830781@qq.com;epfxie@scut.edu.cn;epbzhang@scut.edu.cn

Abstract:

Switching power supply is a kind of high frequency electric energy conversion device and is widely applied to many fields. However, how to model the converter quickly and accurately remains to be solved. A data-driven modeling method is proposed to analyze the power loss of a peak current controlled Boost converter by UC3842 control chip based on neural network(NN). In order to select the key input and output parameters for data-driven model, the operation mechanism of the Boost converter is studied. Then, data-driven modeling method based on neural network is introduced and applied to model the Boost converter. Finally, the input characteristic and the inner loss characteristic are analyzed by the proposed method. Compared with traditional mechanism modeling method, data-driven modeling can bypass the complex internal mechanism and the model established by using the mapping relationship between data has the advantages of high speed and high accuracy.

Key words: Data-driven model, neural network, peak current control Boost converter

CLC Number: