Power quality data evaluation in distribution networks based on data mining techniques

With the increasing amount of data available from the harmonic monitoring systems in the distribution grid, it is becoming more important to evaluate the harmonic data. This paper presents an algorithm using data mining technique, in particular mixture modeling based on the Minimum Message Length (MML) method, to classify the harmonic data into clusters and identify useful patterns within the data. The resulted clusters are applied to distinguish the sources of disturbances and the time schedule of the disturbances in the distribution grid. In addition, the C5.0 algorithm as a supervised learning is used to produce rules about how the measured data is classified into various clusters using decision tree technique. These generated rules can then be utilized to predict which cluster any new data belongs to without calculating again.