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In this paper, a deep learning approach for power quality monitoring in systems with distributed generation sources is presented. The proposed method focuses in the multi-scale analysis of multi-component signals for power quality disturbances classification. The proposed methodology combines a signal processing stage using variational mode decomposition (VMD) to obtain the times scales of multi-component signals, and a deep learning stage using a simple feedforward neural network (FFNN) to classify the disturbances. The simple proposed architecture allows minimum training time of the classification model. In addition, the proposed method is able to classify different disturbance combinations based on a reduced training-set. The proposed VMD-FFNN method is tested using synthetic and simulated signals, and it is compared with other well-known methods based on convolutional and recurrent deep neuronal networks. Finally, the proposed method is assessed using lab measurements in order to shown its performance in a real-world environment. • A multi-component signal analysis with a minimum training class number. • A fast-training process for the managing of different system configurations. • The detection of the disturbance's onset time, through an observation window analysis. [ABSTRACT FROM AUTHOR]

Power quality (PQ) in microgrids is a relevant topic, particularly with the complex dynamic behaviour of disturbances produced by the electronic components intrinsic in these technologies. In this paper, a qualitative-quantitative hybrid approach named multiscale recurrence quantification decomposition (MSRQD) combined with support vector machines (SVM) is proposed for PQ monitoring in microgrid systems. MSRQD combines variational mode decomposition (VMD) with recurrence quantification analysis (RQA). To validate the effectiveness of the proposed method, it is compared against other methods. In addition, three study cases are conducted in a microgrid test system using simulations, and real measured data of a main distribution feeder to prove the classification capability of the proposed method in a real environment. This novel approach, provides qualitative graphics and quantitative statistical indexes for disturbance localisation, disturbance time evolution and disturbance characterisation, as well as 3D visualisation feature space of perturbations that makes easy the interpretation to the system operator. [ABSTRACT FROM AUTHOR]