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Identification of Load Power Quality Characteristics using Data Mining

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conference contribution
posted on 2024-11-14, 09:03 authored by Ali Asheibi, David StirlingDavid Stirling, Duane RobinsonDuane Robinson
The rapid increase in computer technology and the availability of large scale power quality monitoring data should now motivate distribution network service providers to attempt to extract information that may otherwise remain hidden within the recorded data. Such information may be critical for identification and diagnoses of power quality disturbance problems, prediction of system abnormalities or failure, and alarming of critical system situations. Data mining tools are an obvious candidate for assisting in such analysis of large scale power quality monitoring data. This paper describes a method of applying unsupervised and supervised learning strategies of data mining in power quality data analysis. Firstly underlying classes in harmonic data from medium and low voltage (MV/LV) distribution systems were identified using clustering. Secondly the link analysis is used to merge the obtained clusters into supergroups. The characteristics of these super-groups are discovered using various algorithms for classification techniques. Finally the a priori algorithm of association rules is used to find the correlation between the harmonic currents and voltages at different sites (substation, residential, commercial and industrial) for the interconnected supergroups.

History

Citation

This paper was originally published as: Asheibi, A, Stirling, D & Robinson, D, Identification of Load Power Quality Characteristics using Data Mining, Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, 7-10 May 2006, 157-162. Copyright IEEE 2006.

Parent title

Canadian Conference on Electrical and Computer Engineering

Pagination

157-162

Language

English

RIS ID

17678

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