Hardware for classification of power quality problems in three phase system using Microcontroller

Power quality is a growing concern as the number of electric sensitive loads is increasing day by day in all fields. Poor power quality is badly affecting sensitive loads causing instability, malfunctioning, loss of data and great loss of economy. Thus, there is a need to improve power quality for which it is required to identify power quality problems occurring in the power system. This paper presents a low cost hardware to detect and classify most occurring power quality problems in the power system such as voltage sag, swell, interruption and unbalance using PIC microcontroller 18F452 for a 3-phase system. An algorithm is developed and the program is written into the microcontroller for classification. The program checks the conditions of the power quality problems based on the magnitudes of 3-phase voltages and classifies them. The system mainly consists of voltage sensing unit and microcontroller which senses 3-phase voltages, identifies the problem and gives output through LCD display, buzzer and LEDs showing type of power quality problem and p.u. voltage values. The circuit also trips load during power quality problems and connects it during normal conditions. The outputs of the hardware are given for various cases of the power quality problems

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

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