Compressive Informative Sparse Representation-Based Power Quality Events Classification

Abstract

Power quality (PQ) events are referred to any abnormal deviation from the standard sinusoidal behavior of power signals within a power system. PQ events are usually studied by tracking the behavior of voltage signals over observation points of the system. IEEE Standards have defined standard categories for PQ events based on their time behavior. Each class of these events may have different level of importance from different contributors’ perspective (utilities, system operators, or costumers). Due to increasing the usage of sensitive technological loads such as transportation, banking systems, and databases on one hand in addition to the uncertainty injected to the system from aggregation of renewables on the other hand, the fast and reliable PQ events classification is an important monitoring task in the future smart grid. In this paper, combining the theory of sparse recovery with a new high-dimensional convex hull approximation framework we have developed a fast, reliable, and adaptive PQ events classification methodology named “compressive-informative sparse representation-based” PQ events classifier. Unlike usual classification approaches, the proposed classifier does not need any training procedure while due to its linear mathematical formulation acts inherently fast. Moreover, it can be easily adapted to recognize the challenging combined PQ events in addition to any permanent change in the behavior of PQ patterns.