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

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.

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References


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DOI: http://dx.doi.org/10.21622/resd.2015.01.2.294

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Copyright (c) 2016 Hammam Abdelaal Hammam Soliman, Huai Wang, Brwene Salah Abdelkarim Gadalla, Frede Blaabjerg

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu