A highly accurate and fast power quality disturbances classification based on dictionary learning sparse decomposition

This paper proposes a highly accurate and fast power quality disturbances (PQDs) classification using dictionary learning sparse decomposition (DLSD). Firstly, an over-complete dictionary is constructed by combining an identity matrix with a learning dictionary trained by K-SVD algorithm. Secondly, the features and the fuzzy primary classifications of PQDs are obtained by calculating the sparse decomposition coefficients based on the learning dictionary. For being adaptive to sparsity and reducing computational complexity, a fast adaptive matching pursuit (FAMP) using sparsity adaptive algorithm and regularized atom selection is proposed. Then, a decision tree is adopted to accomplish accurate classification by using the estimated features and the pre-classification results. Finally, the proposed approach is tested by PQDs from simulations, IEEE PES database and actual measurements. Moreover, several testing signals, which contain strong noise and frequency deviation, are introduced to further validate DLSD. The results demonstrate that DLSD has a good improvement on computational complexity and classification accuracy when dealing with PQDs classification.