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University of Wollongong

Faculty of Informatics - Papers (Archive)

2009

Harmonic

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Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson Jan 2009

Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson

Faculty of Informatics - Papers (Archive)

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …


Allocated Harmonic Quantities As The Basis For Source Detection, Timothy J. Browne, Sarath Perera, Victor J. Gosbell Jan 2009

Allocated Harmonic Quantities As The Basis For Source Detection, Timothy J. Browne, Sarath Perera, Victor J. Gosbell

Faculty of Informatics - Papers (Archive)

A considerable body of literature examines assessment, from measurements, of whether it is the network or a customer installation which makes the greater contribution to harmonic distortion at a point of common coupling. However, the customer contribution to harmonic distortion at a point of common coupling depends heavily upon the definition chosen for that contribution. For example, expressing contributions as currents instead of voltages or vice versa may lead to large changes in results. Further, it can be shown that the harmonic voltage at the point of common coupling cannot be expressed independently of the network conditions, meaning that the …