Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

Hammam Abdelaal Hammam Soliman, Huai Wang, Brwene Salah Abdelkarim Gadalla, Frede Blaabjerg

Research output: Contribution to journalJournal articleResearchpeer-review

237 Downloads (Pure)

Abstract

In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given. The presented method enables a pure software based approach with high parameter estimation accuracy.
Original languageEnglish
JournalJournal of Renewable Energy and Sustainable Development (RESD)
Volume1
Issue number2
Pages (from-to)294-299
Number of pages6
ISSN2356-8518
Publication statusPublished - Dec 2015

Fingerprint

Condition monitoring
Capacitors
Capacitance
Neural networks
Power electronics
Parameter estimation
Health
Hardware
Costs
Industry

Keywords

  • Capacitor condition monitoring
  • Capacitor health status
  • Capacitance estimation

Cite this

@article{c4c31cc9ba2b45ee9d4cedf96f0fb44b,
title = "Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation",
abstract = "In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given. The presented method enables a pure software based approach with high parameter estimation accuracy.",
keywords = "Capacitor condition monitoring, Capacitor health status, Capacitance estimation",
author = "Soliman, {Hammam Abdelaal Hammam} and Huai Wang and Gadalla, {Brwene Salah Abdelkarim} and Frede Blaabjerg",
year = "2015",
month = "12",
language = "English",
volume = "1",
pages = "294--299",
journal = "Journal of Renewable Energy and Sustainable Development (RESD)",
issn = "2356-8518",
number = "2",

}

Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation. / Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim; Blaabjerg, Frede.

In: Journal of Renewable Energy and Sustainable Development (RESD), Vol. 1, No. 2, 12.2015, p. 294-299.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

AU - Soliman, Hammam Abdelaal Hammam

AU - Wang, Huai

AU - Gadalla, Brwene Salah Abdelkarim

AU - Blaabjerg, Frede

PY - 2015/12

Y1 - 2015/12

N2 - In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given. The presented method enables a pure software based approach with high parameter estimation accuracy.

AB - In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given. The presented method enables a pure software based approach with high parameter estimation accuracy.

KW - Capacitor condition monitoring

KW - Capacitor health status

KW - Capacitance estimation

UR - http://apc.aast.edu/ojs/index.php/RESD/article/view/100

M3 - Journal article

VL - 1

SP - 294

EP - 299

JO - Journal of Renewable Energy and Sustainable Development (RESD)

JF - Journal of Renewable Energy and Sustainable Development (RESD)

SN - 2356-8518

IS - 2

ER -