Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data.
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. (substation, residential, commercial and industrial) for the interconnected supergroups.
Disciplines
Physical Sciences and Mathematics
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