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Robust harmonic detection, classification and compensation method for electric drives based on the sparse FFT and the Mahalanobis distance
ABB Corporate Research, Sweden.
ABB Corporate Research.
ABB Corporate Research.
ABB Corporate Research.
2017 (English)In: IET Electric Power Applications, ISSN 1751-8660, E-ISSN 1751-8679, Vol. 11, no 7, p. 1177-1186Article in journal (Refereed) Published
Abstract [en]

This work presents a method for the robust detection, classification and possible compensation of harmonics in electric drives during real-time operation, with the aim of providing a framework for monitoring and diagnostic without the need of additional hardware. The detection is performed with a sparse fast Fourier transform algorithm, for its lower computational effort when the signals are sparse (which, by definition, contain few meaningful spectral lines). The classification is performed on the results of the signal frequency analysis by means of the Mahalanobis distance concept, improving the robustness and noise rejection properties of the method. The compensation part relies on a family of regulators in parallel, each operating in the rotating reference frame of the harmonic to be cancelled. The theoretical background is followed by a discussion on the implementation and the interaction of the three blocks for a successful real-time operation. The system was tested in laboratory and proved to fulfil the requirements, by running in parallel to a vector control for synchronous machines. It was also found that the method is a useful tool to determine the presence of unknown harmonics in an electric drive system, thus potentially providing early warnings of unexpected failures.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2017. Vol. 11, no 7, p. 1177-1186
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-235008DOI: 10.1049/iet-epa.2016.0843ISI: 000407011900004Scopus ID: 2-s2.0-85026737463OAI: oai:DiVA.org:kth-235008DiVA, id: diva2:1248380
Note

QC 20181001

Available from: 2018-09-14 Created: 2018-09-14 Last updated: 2019-08-02Bibliographically approved

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CiteExportLink to record
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  • apa
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