Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citation (Scopus)

Resumé

In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition monitoring methodologies are rarely adopted by industry due to shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new
condition monitoring methodologies that are based on advanced software and requires no extra hardware could be more attractive to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc-link voltage are used
as training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted to
validate the proposed methodology and the error analysis of the estimated results is also studied.
OriginalsprogEngelsk
TitelProceedings of 2017 IEEE Energy Conversion Congress and Exposition (ECCE)
Antal sider8
ForlagIEEE Press
Publikationsdatookt. 2017
Sider5795-5802
ISBN (Elektronisk)978-1-5090-2998-3
DOI
StatusUdgivet - okt. 2017
Begivenhed2017 IEEE Energy Conversion Congress and Exposition (ECCE) - Cincinnati, Ohio, USA
Varighed: 1 okt. 20175 okt. 2017

Konference

Konference2017 IEEE Energy Conversion Congress and Exposition (ECCE)
LandUSA
ByCincinnati, Ohio
Periode01/10/201705/10/2017

Fingerprint

Condition monitoring
Capacitors
Capacitance
Neural networks
Hardware
Monitoring
Electric potential
Power electronics
Fast Fourier transforms
Error analysis
Industry
Diodes
Aging of materials
Health
Costs
Experiments

Citer dette

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title = "Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems",
abstract = "In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition monitoring methodologies are rarely adopted by industry due to shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of newcondition monitoring methodologies that are based on advanced software and requires no extra hardware could be more attractive to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc-link voltage are usedas training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted tovalidate the proposed methodology and the error analysis of the estimated results is also studied.",
author = "Soliman, {Hammam Abdelaal Hammam} and Pooya Davari and Huai Wang and Frede Blaabjerg",
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language = "English",
pages = "5795--5802",
booktitle = "Proceedings of 2017 IEEE Energy Conversion Congress and Exposition (ECCE)",
publisher = "IEEE Press",

}

Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems. / Soliman, Hammam Abdelaal Hammam; Davari, Pooya; Wang, Huai; Blaabjerg, Frede.

Proceedings of 2017 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE Press, 2017. s. 5795-5802.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

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AU - Davari, Pooya

AU - Wang, Huai

AU - Blaabjerg, Frede

PY - 2017/10

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N2 - In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition monitoring methodologies are rarely adopted by industry due to shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of newcondition monitoring methodologies that are based on advanced software and requires no extra hardware could be more attractive to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc-link voltage are usedas training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted tovalidate the proposed methodology and the error analysis of the estimated results is also studied.

AB - In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition monitoring methodologies are rarely adopted by industry due to shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of newcondition monitoring methodologies that are based on advanced software and requires no extra hardware could be more attractive to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc-link voltage are usedas training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted tovalidate the proposed methodology and the error analysis of the estimated results is also studied.

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