Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

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.