Study on identification of power quality disturbances based on compressive sensing

Abstract

Identification of power quality events is one of key tasks in power system protection. This paper presents a new approach based on compressive sensing (CS) for classifying multiple power quality disturbances (PQD). First, every test event sample of PQD is represented as a sparse linear combination of training event samples using sparse representation. A lower-dimensional random matrix is then applied to both test sample of PQD and a CS-guided sensing matrix derived from training samples to reduce dimensionality of the linear combination expression. A L1-minimization solution method is used to solve the sparse representation of every test sample of PQD. Finally, the object class of the PQD event is determined by the minimum of the residual error between test sample and its sparse representation. Simulation and experiment results show that the proposed CS-based method can effectively extract features of PQD and has a high classification accuracy rate with an average value larger than 95% under noise circumstance for 10 types of PQD.