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  • 1.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Ribeiro, Paulo F.
    Federal University of Itajuba, Av. BPS 1303, 37500 903 Itajuba, Brazil.
    The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification2023In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 214, no Part A, article id 108834Article in journal (Refereed)
    Abstract [en]

    This work investigates the use of advanced signal processing and deep Learning for pattern recognition and classification of signals with power quality disturbances. For this purpose, the continuous wavelet transform is used to generate 2-D images with the time–frequency representation from signals with voltage disturbances. The work aims to use convolutional neural networks to classify this data according to the images’ distortion. In this implementation of artificial intelligence, specific stages of design, training, validation, and testing were carried out for a model elaborated from the scratch and a transfer learning technique with the pre-trained networks SqueezeNet, GoogleNet, and ResNet-50. The work was developed in the MATLAB/Simulink software, all signal processing stages, CNN design, simulation, and the investigated data generation. All steps have their objectives fulfilled, culminating in the excellent execution and development of the research. The results sought high precision for the model from scratch and ResNet-50 in classify the test set. The other two models obtained not-so-high accuracy, and the results are consistent when compared with different methodologies. The main contributions of the paper are: (i) developing a methodology to use DL and transfer learning on the classification of voltage disturbances; (ii) using a 2-D representation that incorporates time and frequency information that characterizes several PQ issues; (iii) conducting a study case that shows the suitability of CNN as a tool for voltage disturbance classification, with specific application for 2-D images. Considerations about the results were pointed out.

  • 2.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Silva, Maise N. S.
    Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá 37500 903 Brazil.
    Ribeiro, Paulo F.
    Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá 37500 903 Brazil.
    Observations on Harmonics Summation in Transmission Systems: Alternative Aggregation Estimation2022In: International Transactions on Electrical Energy Systems, E-ISSN 2050-7038, article id 5313417Article in journal (Refereed)
    Abstract [en]

    The aggregation of harmonic components from different sources is one of the critical and challenging assessments in electric power systems. Harmonic summation analysis and estimation is not a simple task since there will be variations because of the grid complexity, nonlinear sources, and unpredictable behaviour of harmonic currents that affect the results. An evaluation of harmonic summation using alternative methods to calculate the harmonic composition at any network point is suggested. A typical arrangement of transmission grids was modelled and used to simulate the results. This paper aims to highlight the results obtained by these alternative methods of harmonic summation and show the role of this type of analysis in transmission systems planning. The contributions are (a) illustrate how alternative methods of harmonic summation can be applied to investigate harmonic aggregation from different sources; (b) provide a case study that also discusses the harmonic aggregation effects with different locations of sources and component phase angle shifting; (c) show comparison and correlation between those alternative summations calculations with a standardized and firmly adopted method (proposed by IEC 61000-3-6). The software MATLAB/Simulink performs simulation and analysis. Finally, the work discusses the findings.

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