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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. Index Terms -Power quality, disturbance classification, compressive sensing, random matrix, dimensionality reduction projection. *

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The decomposition process for the extraction of harmonics components from compressed power quality data was required under the frame of Shannon sample theory, but it increased the complexity of data procedure. A compressed sampling matching pursuit (CoSaMP) method was presented to detect harmonics from compressive sensing (CS)-based compressed power data sequence avoiding decompression pretreatment. First, the compressed power data sequence was obtained from original power signal under the manipulation of a measurement matrix based on CS theory. A vector served as a proxy for the original power signal was formed because of energy approximation between them. The largest harmonics component of the proxy was located and extracted using a least-square algorithm. Then, the proxy was updated to reflect the current residual and was used to estimate the next largest harmonics component. This process was repeated until all of the harmonics components were recovered. The CoSaMP harmonic detection method can detect harmonics components from compressed power quality data directly. It has great potential to reduce the burden of sampling devices and save intermediate storage space. Simulation results show the proposed method is effective for both harmonic and inter-harmonic detection in power system.