Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)

Abstract

In modern design of power electronic converters, reliability of DC-link capacitors is an essential aspect to be considered. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. The existing condition monitoring methods suffer from shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new condition monitoring methodologies that are based on advanced software algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper, a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC/DC/AC converter. The estimation is based on the usage of single phase output current and dc-link voltage ripple. The impact of training data type, source and amount are also investigated for estimation accuracy analysis. Experimental validation of the proposed method is also conducted.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia)
Number of pages5
PublisherIEEE Press
Publication dateJun 2017
Pages196-201
ISBN (Print)978-1-5090-5157-1
DOIs
Publication statusPublished - Jun 2017
Event2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia) - Kaohsiung, Taiwan, Province of China
Duration: 3 Jun 20177 Jun 2017

Conference

Conference2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia)
CountryTaiwan, Province of China
CityKaohsiung
Period03/06/201707/06/2017

Fingerprint

Diodes
Condition monitoring
Capacitance
Neural networks
Capacitors
Power electronics
Aging of materials
Health
Hardware
Monitoring
Electric potential
Costs

Cite this

Soliman, H. A. H., Abdelsalam, I., Wang, H., & Blaabjerg, F. (2017). Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System. In Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia) (pp. 196-201). IEEE Press. https://doi.org/10.1109/IFEEC.2017.7992442
Soliman, Hammam Abdelaal Hammam ; Abdelsalam, Ibrahim ; Wang, Huai ; Blaabjerg, Frede. / Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System. Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia). IEEE Press, 2017. pp. 196-201
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title = "Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System",
abstract = "In modern design of power electronic converters, reliability of DC-link capacitors is an essential aspect to be considered. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. The existing condition monitoring methods suffer from shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new condition monitoring methodologies that are based on advanced software algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper, a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC/DC/AC converter. The estimation is based on the usage of single phase output current and dc-link voltage ripple. The impact of training data type, source and amount are also investigated for estimation accuracy analysis. Experimental validation of the proposed method is also conducted.",
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Soliman, HAH, Abdelsalam, I, Wang, H & Blaabjerg, F 2017, Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System. in Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia). IEEE Press, pp. 196-201, 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), Kaohsiung, Taiwan, Province of China, 03/06/2017. https://doi.org/10.1109/IFEEC.2017.7992442

Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System. / Soliman, Hammam Abdelaal Hammam; Abdelsalam, Ibrahim; Wang, Huai; Blaabjerg, Frede.

Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia). IEEE Press, 2017. p. 196-201.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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T1 - Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System

AU - Soliman, Hammam Abdelaal Hammam

AU - Abdelsalam, Ibrahim

AU - Wang, Huai

AU - Blaabjerg, Frede

PY - 2017/6

Y1 - 2017/6

N2 - In modern design of power electronic converters, reliability of DC-link capacitors is an essential aspect to be considered. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. The existing condition monitoring methods suffer from shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new condition monitoring methodologies that are based on advanced software algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper, a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC/DC/AC converter. The estimation is based on the usage of single phase output current and dc-link voltage ripple. The impact of training data type, source and amount are also investigated for estimation accuracy analysis. Experimental validation of the proposed method is also conducted.

AB - In modern design of power electronic converters, reliability of DC-link capacitors is an essential aspect to be considered. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. The existing condition monitoring methods suffer from shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new condition monitoring methodologies that are based on advanced software algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper, a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC/DC/AC converter. The estimation is based on the usage of single phase output current and dc-link voltage ripple. The impact of training data type, source and amount are also investigated for estimation accuracy analysis. Experimental validation of the proposed method is also conducted.

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Soliman HAH, Abdelsalam I, Wang H, Blaabjerg F. Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System. In Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia). IEEE Press. 2017. p. 196-201 https://doi.org/10.1109/IFEEC.2017.7992442