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

Hammam Soliman, Pooya Davari, Huai Wang, Frede Blaabjerg

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    45 Scopus citations

    Abstract

    Reliability of dc-link capacitors in modern design of power electronic converters is an important aspect that needs to be considered. The requirement of applying condition monitoring for health status estimation in many reliability-critical applications have been a focused demand. The existing capacitor condition monitoring methodologies are suffering from shortcomings such as, low estimation accuracy, extra hardware, and increased cost, and thereby, they are rarely adopted by industry. Therefore, development of new methods that are based on advanced software algorithms and data processing techniques requiring no extra hardware will be more attractive to industry. In this paper, a condition monitoring methodology is proposed and applied on the dc-link capacitor in a three phase Front-End diode bridge motor drive. The proposed condition monitoring methodology estimates the capacitance value of the dc-link capacitor based on Artificial Neural Network (ANN) algorithm. Two ANNs (ANN1 and ANN2) are proposed, trained and evaluated based on time-domain and frequency-domain parameters. Experiments are conducted to validate the proposed methodology and the effectiveness of the proposed method is examined through an error analysis.

    Original languageBritish English
    Title of host publication2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5795-5802
    Number of pages8
    ISBN (Electronic)9781509029983
    DOIs
    StatePublished - 3 Nov 2017
    Event9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017 - Cincinnati, United States
    Duration: 1 Oct 20175 Oct 2017

    Publication series

    Name2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
    Volume2017-January

    Conference

    Conference9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017
    Country/TerritoryUnited States
    CityCincinnati
    Period1/10/175/10/17

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