Power system state recognition using data mining algorithms

This paper describes an approach to identify the state of a power system using Data Mining Algorithms. With the advent of Phasor Measurement Units (PMU), a huge amount of power system data is being stored day-to-day in the Phasor Data Collectors (PDCs). Knowledge discovery and machine learning techniques can make use of this data to extract valuable information and interesting patterns in these databases. In this paper, Symbolic Aggregate ApproXimation (SAX) is used to convert the time stamped phasor data from the PMUs into symbolic strings and Data Mining (DM) algorithms are used to predict the current state of a power system. Along with the state, the location and cause of disturbance is also identified in minimal time. The effectiveness of different DM algorithms for determining state of a power system is also shown using the measures of accuracy, precision and recall.