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.
This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances.
In this study, a novel method based on µ analysis is presented to search for the upper/lower bounds of uncertainty parameters in microgrids (MGs). It is well known that uncertainty parameters have important effects in a MG, and they may cause instability. Previous studies have mainly focused on identifying the stability of a MG with its uncertainty parameters, but they did not address the problem of the upper/lower bounds of uncertainty parameters, i.e., how far the uncertainty parameters can be extended while the system remains stable in the small-signal sense. Thus, we developed an approach for identifying the bounds of uncertainty in MGs. In the current paper, first, a method is proposed for linear fractional transformation (LFT) configuration to express the uncertainty parameters, which makes the stability of the nominal MG system independent of any extension of the bounds. An algorithm based on this configuration is then designed to find the upper/lower bounds for both single parameter and multiple uncertainty parameters in a MG. Finally, the two cases are discussed, and the accuracy of the proposed method is confirmed using the conventional eigenvalue method.
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