Data‐driven Buck converter model identification method with missing outputs

A data‐driven Buck converter model identification method is proposed to deal with missing (incomplete) outputs, which is robust to the data length and percentage of missing data. A nuclear norm based convex optimization problem instead of linear interpolation, to guarantee the recovered missing data...

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Bibliographic Details
Published in:IET control theory & applications Vol. 18; no. 14; pp. 1825 - 1835
Main Authors: Hou, Jie, Zhang, Xinhua, Wang, Huiming, Wang, Shiwei
Format: Journal Article
Language:English
Published: Stevenage John Wiley & Sons, Inc 01.09.2024
Wiley
Subjects:
Buck converters
Convexity
Datasets
DC–DC power convertors
Identification
Identification methods
Mathematical models
Methods
Missing data
modelling
Optimization
Optimization techniques
Parameter identification
Performance evaluation
Sensors
Systems design
ISSN:1751-8644, 1751-8652
Online Access:Get full text
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Summary:A data‐driven Buck converter model identification method is proposed to deal with missing (incomplete) outputs, which is robust to the data length and percentage of missing data. A nuclear norm based convex optimization problem instead of linear interpolation, to guarantee the recovered missing data satisfying the potential model structured low‐rank character, is constructed to estimate missing outputs. The alternating direction method of multiplier strategy is used to solve the nuclear norm based convex optimization problem. In this way, the high‐quality missing data can be estimated, even for short data length and high percentage of missing data. Based on the recovered data, the subspace identification method provides accurate estimates of the structure and parameter of the Buck converter synchronously. By applying the proposed method to a Buck converter, experimental results demonstrate its effectiveness. A data‐driven Buck converter model identification method is proposed in deal with missing (incomplete) outputs, which is robust to the data length and percentage of missing data. By applying the proposed method to a Buck converter, experimental results demonstrate its effectiveness.
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content type line 14
ISSN:1751-8644
1751-8652
DOI:10.1049/cth2.12728