Effinformer: A Deep-Learning-Based Data-Driven Modeling of DC-DC Bidirectional Converters

This article presents a data-driven modeling method founded on deep learning (DL) to precisely model and identify a dc-dc bidirectional converter that is widely used in renewable energy systems (RESs). In contrast to the circuit-theory-based methods that require dealing with a high number of circuit...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 72; pp. 1 - 13
Main Authors: Shang, Qianyi, Xiao, Fei, Fan, Yaxiang, Kang, Wei, Qin, Haochen, Wang, Ruitian
Format: Journal Article
Language:English
Published: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Analytical models
Circuits
Complexity
Computational modeling
Data-driven modeling
dc-dc bidirectional converter
Deep learning
deep learning (DL)
Distillation
informer
Model accuracy
Outliers (statistics)
Predictive models
self-attention
Time series analysis
Visualization
ISSN:0018-9456, 1557-9662
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article presents a data-driven modeling method founded on deep learning (DL) to precisely model and identify a dc-dc bidirectional converter that is widely used in renewable energy systems (RESs). In contrast to the circuit-theory-based methods that require dealing with a high number of circuit parameters and result in models that are often complex and costly, DL-based approaches yield more parsimonious models. However, the typical DL schemes still have limitations in terms of accuracy, stability, and computational complexity. To this end, we propose an end-to-end DL framework named efficient Informer (Effinformer), which leverages sparse self-attention, a novel dilated causal convolution-based distilling operation, and an enhanced decoder to minimize the computational complexity while improving modeling accuracy and speed. Compared to existing modeling techniques and current state-of-the-art DL techniques, the proposed Effinformer exhibits superior performance through experimental results and analysis. Then, we further extend the proposed network to long-term prediction scenarios to demonstrate its excellent generalization ability and elegant robustness. Extensive experiments show that Effinformer is more conducive to enhancing prediction accuracy and reliability from the perspective of handling ripple interference and outliers. This characteristic makes it highly advantageous for practical engineering applications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3318701