Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network

The classification of harmonic source types is a necessary step to alleviate harmonic pollution. This study proposes a method for the harmonic source classification based on 2-D image-matrix transformation (IMT) and deep convolutional neural network (CNN). The method firstly converts the V-I waveforms of different harmonic source types into a matrix by IMT, which was then applied to express the V-I waveform of each harmonic source as an input CNN model. Thereafter, features were extracted automatically from the harmonic source IMT matrix by the CNN model. Finally, favorable results are achieved after identifying the type of harmonic source via a classifier. In case study, a number of verifications were performed on the proposed method. Moreover, compared with existing harmonic source classification methods, the proposed strategy in this study not only avoids extracting features through expert knowledge, but also reduces the amount of data required for the harmonic source classification, and achieves higher accuracy than other methods.

Abstract licence: CC BY-NC-ND

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