Power quality disturbances classification based on curvelet transform

Abstract This article presents a novel method for power quality disturbances (PQDs) classification based on curvelet transform (CT), locality preserving projection (LPP), and multi-class support vector machine (MCSVM). Initially, PQD signals are converted into a two-dimensional image and then feature extracted using CT. The inspiration for this method is based on detailed information of CT. The fast discrete CT is a newly developed transformation and has distinguished features when compared to other transforms, which define the scale, angle, and orientation. The curvelet coefficients have different frequency bands. The lowest frequency band roughly contains image information. The highest frequency band represents the noisy information and remaining holds edge information. In this research work, initial three frequency bands are considered as PQD features. The extracted features are reshaped and reduced dimensionally using LPP. Finally, MCSVM is used for classification of single and combined PQDs. Eight types of single and combined PQ disturbances are considered for classification. The proposed method is tested on both synthetic and real PQ disturbances data, and 99.92 and 99.8% classification accuracy are achieved without and with noise, respectively. Results validate the correctness and robustness of the proposed method in the classification of single and combined PQDs under noiseless and noisy environments.

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