| Abstract: |
| There is growing interest in power quality issues due to wider developments in power delivery engineering. In
order to maintain good power quality, it is necessary to detect and monitor power quality problems. The power
quality monitoring requires storing large amount of data for analysis. This rapid increase in the size of databases has
demanded new technique such as data mining to assist in the analysis and understanding of the data. This paper
presents the classification of power quality problems such as voltage sag, swell, interruption and unbalance using
data mining algorithms: J48, Random Tree and Random Forest decision trees. These algorithms are implemented
on two sets of voltage data using WEKA software. The numeric attributes in first data set include 3-phase RMS
voltages at the point of common coupling. In second data set, three more numeric attributes such as minimum,
maximum and average voltages, are added along with 3-phase RMS voltages. The performance of the algorithms is
evaluated in both the cases to determine the best classification algorithm, and the effect of addition of the three
attributes in the second case is studied, which depicts the advantages in terms of classification accuracy and
training time of the decision trees. |
| Key words: Power quality problems, Classification, Data mining, Decision trees, J48, Random tree, Random forest, WEKA |
| DOI：10.1186/s41601-018-0103-3 |
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| Fund: |
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