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  • 1.
    A. Oliveira, Roger
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    S. Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Deep Learning for Power Quality with Special Reference to Unsupervised Learning2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 935-939, article id 10417Conference paper (Refereed)
  • 2.
    Almeida, Gabriel C. S.
    et al.
    Federal University of Itajubá, Itajubá, Brazil.
    De Souza Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Silva, Maise N. S.
    Federal University of Itajubá, Itajubá, Brazil.
    Zambroni de Souza, Antonio Carlos
    Federal University of Itajubá, Itajubá, Brazil.
    Ribeiro, Paulo Fernando
    Federal University of Itajubá, Itajubá, Brazil.
    The Need of Normative Technologies for Smart Living Cities2022In: Interdisciplinary and Social Nature of Engineering Practices: Philosophy, Examples and Approaches / [ed] Antonio Carlos Zambroni de Souza; Maarten J. Verkerk; Paulo Fernando Ribeiro, Springer, 2022, Vol. 61, p. 283-309Chapter in book (Refereed)
  • 3.
    de Oliveira, Roger Alves
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    De Souza Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math H.J.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    de Carlí, Miguel P.
    CGT Eletrosul Brazil.
    Analysing Waveform Distortion in Wind Power Plants by a Deep Learning-Based Graphical Tool2022In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    This work shows an application of a deep learning-based graphical tool for analyzing waveform distortion in wind power plants. The tool consists of a deep autoencoder followed by a clustering algorithm. Previous applications of such a tool have covered harmonic emission which follows daily patterns. The challenge of measurements in wind power plants is the intermittence of the power production, which can vary in a time frame of minutes and hours. To this point, this work proposes a modification of a DL method presented in the literature to address measurements from wind power plants. The method can automatically obtain the number of clusters. The method is applied to harmonic measurements from H2 to H50 and active power in a Brazilian wind power plant. The graphical results allowed obtaining the correlation between patterns of odd and even current harmonic with the active power generated by a wind power plant.

  • 4.
    de Oliveira, Roger Alves
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Nakhodchi, Naser
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    De Souza Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Deep Learning Graphical Tool Inspired by Correlation Matrix for Reporting Long-Term Power Quality Data at Multiple Locations of an MV/LV Distribution Grid2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 609-613, article id 10324Conference paper (Refereed)
  • 5.
    de Oliveira, Roger Alves
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Salles, Rafael S.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    de Carli, Miguel P.
    Eletrobras at the Generation Departmen in Florianópolis, Brazil.
    Leborgne, Roberto Chouhy
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Harmonic Anomalies Due to Geomagnetically Induced Currents as a Potential Cause of Protection Mal-Trips at the South Atlantic Anomaly Area2024In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 39, no 2, p. 1124-1136Article in journal (Refereed)
  • 6.
    De Souza Salles, Rafael
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Almeida, Gabriel C. S.
    Institute of Electrical and Energy Systems, Federal University of Itajuba, Itajuba, Brazil.
    Ribeiro, Paulo F.
    Institute of Electrical and Energy Systems, Federal University of Itajuba, Itajuba, Brazil.
    Classification of Scalogram Signatures for Power Quality Disturbances Using Transfer Learning2022In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    The electrical power systems have gone through a process of transformations that will remain characterized by a wide penetration of renewable sources, electronic devices, and computerization. In this context, Power Quality (PQ) is associated with several challenges for the sector, presenting new issues and new scenarios for old problems. Signal processing (SP) plays an essential role in PQ applications as a tool that helps measure, characterize, and visualize electrical grid disturbances. At the same time, artificial intelligence (AI) is becomming more and more useful to classification tasks regarding PQ disturbances . This work aims to employ a transfer learning methodology for PQ disturbances classification. Wavelet scalograms of the signal are created using CWT for feature extraction of time-frequency signatures. The 2-D images of this representation are used to train and test pre-trained CNN models’ performance. The work aims to contribute to PQ disturbances classification through innovative methods and assess the performance of different CNNs models that have a significant role in image classification. The performance of four network models is assessed: ResNet-18, VGG-19, Inception-v3, and ResNet-101. Discussion and consideration about the results provide evaluation of the methodology.

  • 7.
    De Souza Salles, Rafael
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Interharmonic Analysis for Static Frequency Converter Station Supplying a Swedish Catenary System2022In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    The paper aims to present a waveform distortion analysis focused on interharmonics in measurements from a 70 kV busbar feeding a traction supply substation with four static frequency converters (SFC). The substation supplies a Swedish catenary system from 50 Hz public grid to 15 kV 16 ⅔ Hz. The paper assesses the interharmonics for different scenarios regarding the point of a connection configuration between the traction substation and the upstream grid, as well as a change in the number of SFCs connected in the substation. The IEC 61000-4-7 grouping method and spectrograms were used to illustrate the issue. The significant presence of interharmonics calls attention to the subject in railway application. The total indexes help to evaluate the broad picture of the phenomena. The work contributes to the waveform distortion and interharmonics in railway systems studies.

  • 8.
    De Souza Salles, Rafael
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Mariscotti, Andrea
    Department of Electrical, Electronics and Telecommunication, Engineering and Naval Architecture (DITEN), University of Genova, Genova, Italy.
    Waveform Distortion Emission Assessment on Pantograph Measurements from Low-Frequency Railway Electrification2022In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    The paper aims to provide a waveform distortion assessment on pantograph current measurements. The data analyzed is regarding a 15 kV 16.7 Hz catenary system from Switzerland. The paper provides a characterization of the rolling stock emission content for different operation modes during a commercial utilization of the vehicle, approaching traditional harmonics, interharmonic and supraharmonic phenomena. In addition, the work provides an adaptation on the interharmonic processing proposed by the IEC 61000-4-7 regarding the 16.7 Hz fundamental frequency. The results contribute to the waveform distortion emission on pantograph measurements subject and railway systems investigation in general.

  • 9.
    Ribeiro, Paulo F.
    et al.
    Federal University of Itajubá, Brazil.
    Salles, Rafael S.Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Distributed Energy Storage in Urban Smart Grids2023Collection (editor) (Other academic)
  • 10.
    Ribeiro, Paulo Fernando
    et al.
    Federal University of Itajubá, Itajubá, Brazil.
    De Souza Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Introduction2022In: Interdisciplinary and Social Nature of Engineering Practices: Philosophy, Examples and Approaches / [ed] Antonio Carlos Zambroni de Souza; Maarten J. Verkerk; Paulo Fernando Ribeiro, Springer, 2022, Vol. 61, p. 3-19Chapter in book (Other academic)
  • 11.
    Ribeiro, Paulo Fernando
    et al.
    Federal University of Itajubá, Itajubá, Brazil.
    Verkerk, Maarten J.
    Eindhoven University of TechnologyEindhovenNetherlands.
    Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Toward a Holistic Normative Design2022In: Interdisciplinary and Social Nature of Engineering Practices: Philosophy, Examples and Approaches / [ed] Antonio Carlos Zambroni de Souza; Maarten J. Verkerk; Paulo Fernando Ribeiro, Springer, 2022, Vol. 61, p. 57-77Chapter in book (Other academic)
  • 12.
    S. Salles, Rafael
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Asplund, Rebecca
    Trafikverket, Sweden.
    Survey of Harmonic Distortion Measurements from Customer Grid Supply in Trains2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 1680-1684, article id 0658Conference paper (Refereed)
  • 13.
    S. Salles, Rafael
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Mariscotti, Andrea
    University of Genova, Italy.
    Psophometric Indices Analysis for Waveform Distortion from Rolling Stocks in Electrified Traction Systems2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 1685-1689, article id 0659Conference paper (Refereed)
  • 14.
    Sadeghi, Babak
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Westerlund, Per
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    S. Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Wilén, Jonna
    Umeå University, Sweden.
    Radiated Emissions from an Electric Railway: Review of Methods and Measurements mainly from 9 Khz To 150 Khz2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 3694-3698, article id 1347Conference paper (Refereed)
  • 15.
    Salles, Rafael De Souza
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Review of Waveform Distortion Interactions Assessment in Railway Power Systems2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 14, article id 5411Article, review/survey (Refereed)
    Abstract [en]

    This work aims to cover the measurement, modeling, and analysis of waveform distortions in railway power systems. It is focused on waveform distortion as a phenomenon that includes harmonic distortion, interharmonic distortion, and supraharmonics. A comprehensive view of the interactions of waveform distortions in railway systems is needed, together with a grid perspective of power quality incorporating all aspects, sources, propagation, requirements, and effects. It is understood that the communities interested or involved in the subject of railway power systems would benefit from an integrated overview of the literature on the complex problem of waveform distortion. The literature review is divided into four categories: characterization and measurements, modeling, the application of artificial intelligence, and specific issues. For each category of work, the contributions are highlighted, and a discussion on opportunities, gaps, and critical observations is provided. The work successfully builds a framework for the subject with two main characteristics; the review is informative and propositional, providing a road map of opportunities for future works. Some aspects and recommendations can be highlighted. Suggestions for future works and research practices on waveform distortion in electrical transportation are offered.

    Download full text (pdf)
    fulltext
  • 16.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    de Oliveira, Roger Alves
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Mariscotti, Andrea
    Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy.
    Analytics of Waveform Distortion Variations in Railway Pantograph Measurements by Deep Learning2022In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 71, article id 2516211Article in journal (Refereed)
    Abstract [en]

    Waveform distortion in general represent a widespread problem in electrified transports due to interference, service disruption, increased losses and ageing of components. Given the multitude of moving sources and the extremely variable operating conditions, short time records must be considered for analysis, and this increases in turn its complexity, from which the need for effective automated processing, as offered by a deep learning (DL) approach. This paper proposes an application of unsupervised DL to measurements of railway pantograph quantities to identify waveform distortion patterns. Data consists of pantograph current from a Swiss 15 kV 16.7 Hz railway system. Three DL input types are considered: waveforms, harmonic spectra, and supraharmonic spectra. The applied DL method applied is the deep autoencoder (DAE) followed by feature clustering, using techniques to define a suitable number of clusters. Short-term distortion is evaluated over sub-10 min intervals of harmonic and supraharmonic spectra down to sub-second intervals. Results are explained among others by connecting the distribution of the clusters (determined by self-supervised method) to the dynamic operating conditions of the rolling stock. Resulting DAE performance are superior in terms of accuracy and comprehensiveness of spectral components compared to a more traditional principal component analysis (PCA) that was chosen as reference for comparison.

  • 17.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Ribeiro, Paulo F.
    Federal University of Itajubá, Brazil.
    Introduction: Energy transition, urban grids, and energy storage2023In: Distributed Energy Storage in Urban Smart Grids / [ed] Paulo F. Ribeiro; Rafael S. Salles, Institution of Engineering and Technology, 2023, p. 1-14Chapter in book (Other academic)
  • 18.
    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.

  • 19.
    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.

  • 20.
    Silva, Maise N.S.
    et al.
    Federal University of Itajubá, Brazil.
    Salles, Rafael S.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Ribeiro, Paulo F.
    Federal University of Itajubá, Brazil.
    International experience on distributed energy storage2023In: Distributed Energy Storage in Urban Smart Grids / [ed] Paulo F. Ribeiro; Rafael S. Salles, Institution of Engineering and Technology , 2023, p. 65-92Chapter in book (Other academic)
1 - 20 of 20
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