Expert system for voltage dip classification and analysis

Abstract

The increasing amount of data obtained by power quality monitors and the need for better understanding of power system disturbances require new analysis tools. This paper presents an expert system that is able to classify different types of voltage dips according to the underlying causes (i.e. events) and offer useful information in terms of power quality. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips (fault-induced, transformer saturation, induction motor starting), explains the changes in the voltage dip magnitude (change in the system, change in the fault type, transformer saturation, motor load influence) and separates interruptions into non-fault and fault-induced. A method is proposed for event-based classification, where a segmentation algorithm is first applied to divide waveforms into several possible events. Kalman filtering is employed to model the waveforms and the residuals of the model are used for segmentation. The expert system is tested using real measurements and the results show that the system enables fast and accurate analysis of data from power quality monitors.