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de Oliveira, Roger AlvesORCID iD iconorcid.org/0000-0001-5845-5620
Alternative names
Publications (10 of 33) Show all publications
Salles, R. S., de Oliveira, R. A., Rönnberg, S. K. & Mariscotti, A. (2024). Data-driven assessment of VI diagrams for inference on pantograph quantities waveform distortion in AC railways. Computers & electrical engineering, 120(Part B), Article ID 109730.
Open this publication in new window or tab >>Data-driven assessment of VI diagrams for inference on pantograph quantities waveform distortion in AC railways
2024 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 120, no Part B, article id 109730Article in journal (Refereed) Published
Abstract [en]

This work proposes an application of unsupervised deep learning (DL) on 2-D images containing VI diagrams of measured railway pantograph quantities to find patterns in operating conditions (OCs) and waveform distortion. Measurement data consist of pantograph voltage and current measurements from a Swiss 15 kV 16.7 Hz commercial locomotive and a French 2x25 kV 50 Hz test-dedicated locomotive, containing more than 4000 records of 5-cycle snippets for each system. The variational autoencoder (VAE), followed by feature clustering, finds patterns in the input data. Each cluster captures patterns from the VI diagrams, which contain information from current and voltage waveshapes and sub-second variations. The time-domain admittance allows inference about the rolling stock (RS) operation and the waveform distortion spectra, including harmonics and supraharmonics characteristics from both RS and traction supply. The VAE successfully performs data embedding using only 16 channels in the latent space. The effectiveness of the method is quantified by means of the mean square reconstruction error (never larger than 1.5% and equal to 0.31% and 0.33% on average for the Swiss and French case, respectively). The t-SNE visualization confirms that overlapping of clusters is negligible, with a percentage of “misplaced” cluster points of 2.18% and 2.50%, again for the Swiss and French case, respectively. The computation time for the VAE prediction could be brought to some tens of ms representing a performance reference for future implementations. The proposed VI diagram assessment covers emissions for different OCs, rapid changes in power supply conditions, and background distortion caused by other trains on the same line, including line and impedance changes due to the moving load. In this perspective physical justification is found by domain knowledge integration for the identified clusters. A concluding discussion regarding advantages, limitations, and potential improvements or diversification is also included.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Dimension reduction, Pattern analysis, Power quality, Power system harmonics, Load monitoring, Guideway transportation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-110282 (URN)10.1016/j.compeleceng.2024.109730 (DOI)001368585500001 ()2-s2.0-85205289816 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-11-11 (joosat);

Full text license: CC BY 4.0;

Funder: Swedish Transport Administration; 

Available from: 2024-10-08 Created: 2024-10-08 Last updated: 2024-12-12Bibliographically approved
Alves de Oliveira, R., Malfoy, A. & Rönnberg, S. K. (2024). Deep Anomaly Detection of Voltage Waveform Distortion in the Transmission Grid due to Geomagnetically Induced Currents. IEEE Transactions on Instrumentation and Measurements, 73, Article ID 2509212.
Open this publication in new window or tab >>Deep Anomaly Detection of Voltage Waveform Distortion in the Transmission Grid due to Geomagnetically Induced Currents
2024 (English)In: IEEE Transactions on Instrumentation and Measurements, ISSN 0018-9456, Vol. 73, article id 2509212Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-100715 (URN)10.1109/TIM.2024.3366271 (DOI)001178180500018 ()2-s2.0-85185388685 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-09 (joosat);

Available from: 2023-08-24 Created: 2023-08-24 Last updated: 2024-11-20Bibliographically approved
Salles, R. S., Oliveira, R. A. & Rönnberg, S. K. (2024). Exploring Daily Variation Patterns on Total Harmonic Distortion Long-Term Measurements in Traction Converter Stations using Data Analytics. In: Xianyong Xiao; Yang Wang (Ed.), Proceedings - 2024 21st International Conference on Harmonics and Quality of Power, ICHQP 2024: . Paper presented at 2024 21st International Conference on Harmonics and Quality of Power, Oct 15-18, 2024, Chengdu, China. IEEE Computer Society
Open this publication in new window or tab >>Exploring Daily Variation Patterns on Total Harmonic Distortion Long-Term Measurements in Traction Converter Stations using Data Analytics
2024 (English)In: Proceedings - 2024 21st International Conference on Harmonics and Quality of Power, ICHQP 2024 / [ed] Xianyong Xiao; Yang Wang, IEEE Computer Society , 2024Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2024
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-111227 (URN)10.1109/ICHQP61174.2024.10768823 (DOI)2-s2.0-85213320186 (Scopus ID)
Conference
2024 21st International Conference on Harmonics and Quality of Power, Oct 15-18, 2024, Chengdu, China
Note

ISBN for host publication: 979-8-3503-8256-3

Available from: 2025-01-07 Created: 2025-01-07 Last updated: 2025-01-07
de Oliveira, R. A., Salles, R. S., Rönnberg, S., de Carli, M. P. & Leborgne, R. C. (2024). Harmonic Anomalies Due to Geomagnetically Induced Currents as a Potential Cause of Protection Mal-Trips at the South Atlantic Anomaly Area. IEEE Transactions on Power Delivery, 39(2), 1124-1136
Open this publication in new window or tab >>Harmonic Anomalies Due to Geomagnetically Induced Currents as a Potential Cause of Protection Mal-Trips at the South Atlantic Anomaly Area
Show others...
2024 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 39, no 2, p. 1124-1136Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-100716 (URN)10.1109/TPWRD.2024.3353487 (DOI)001193300900036 ()2-s2.0-85182951668 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-03 (joosat);

Funder: Swedish Energy Agency and Rönnbäret Foundation (24549);

This article has previously appeared as a manuscript in a thesis.

Available from: 2023-08-24 Created: 2023-08-24 Last updated: 2024-11-20Bibliographically approved
Shimi, S. L., de Oliveira, R. A., Kumar, A. & Kumar, P. (2024). Impacts Due to Vehicle-to-Grid and Solar Photovoltaic Integration with the Grid: A Review. In: Ajay Kumar; Parveen Kumar; Shimi Sudha Letha; Arif Sarwat; Mohd Tariq (Ed.), Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling (pp. 55-70). John Wiley & Sons
Open this publication in new window or tab >>Impacts Due to Vehicle-to-Grid and Solar Photovoltaic Integration with the Grid: A Review
2024 (English)In: Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling / [ed] Ajay Kumar; Parveen Kumar; Shimi Sudha Letha; Arif Sarwat; Mohd Tariq, John Wiley & Sons, 2024, p. 55-70Chapter in book (Other academic)
Abstract [en]

The rapid growth of electric vehicles (EVs) and solar photovoltaic (PV) installations to achieve zero emission has prompted an intensive investigation into their integration with the electrical grid. This paper conducts a thorough review of the multifaceted impacts arising from the confluence of EVs and PV systems with the grid, with a primary focus on voltage stability, power quality, and the associated challenges. The study also explores the synergistic effects of PV and EV integration, emphasizing the need for a holistic approach to address the complexities introduced by their combined presence on the grid. The paper explores various mechanisms to enhance voltage stability, including advanced control strategies, grid reinforcement measures, and the integration of energy storage systems. Additionally, it scrutinizes the role of bidirectional power converters in managing power flow bidirectionally between battery EV, PV systems, and the electric grid, ensuring optimal utilization of resources and mitigating potential grid disturbances. Power quality is another pivotal aspect examined in this review. The integration of EVs and PV systems introduces harmonics, voltage sags, and other power quality issues that can compromise the reliability of the grid. The paper outlines the impact of these disturbances on the grid and explores the role of power conditioners in enhancing power quality concerns. Furthermore, it emphasizes the significance of big data analytics in handling the vast amount of data generated by the interconnected EVs and PV systems, enabling effective monitoring, analysis, and prediction of grid behavior. The synergistic impact of solar PV system and the battery EV integration on the grid is a focal point of this review. By investigating the interdependencies and interactions between these two technologies, the paper provides insights into the challenges and opportunities presented by their joint presence. Strategies to optimize the coexistence of PV and EVs, such as intelligent charging algorithms and demand-side management, are explored to ensure a symbiotic relationship that enhances grid resilience and efficiency. The review underscores the imperative need for power conditioners to address the challenges associated with EV and PV integration. Whether it be in the form of advanced inverters, active filters, or voltage regulators, these devices play a vital role in stabilizing the grid and ensuring quality of power.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
Keywords
vehicle-to-grid, solar photovoltaics, grid integration, voltage stability, power quality
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-111473 (URN)10.1002/9781394225040.ch2 (DOI)2-s2.0-85215554746 (Scopus ID)
Note

ISBN for host publication: 9781394225019

Available from: 2025-01-31 Created: 2025-01-31 Last updated: 2025-01-31Bibliographically approved
de Oliveira, R. A. (2023). Applications of Unsupervised Deep Learning for Analysing Time-Varying Power Quality Big Data. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Applications of Unsupervised Deep Learning for Analysing Time-Varying Power Quality Big Data
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Continuous power quality monitoring allows grid stakeholders to obtain information about the performance of the network and costumer facilities. Moreover, the analysis of continuous monitoring allows researchers to obtain knowledge on power quality phenomena. Power quality measurements result in a large amount of data. Power quality data can be classified as big data, not only for its volume, but also for the other complexities: velocity, variety, and veracity. Manual analysis of power quality data is possible but time-consuming. Moreover, data reports based on standardized indexes and classical statistical techniques might hide important information of the time-varying behaviour in power quality measurements. Artificial intelligence plays a role in providing automatic tools for proper analytics of big data.  A subset of artificial intelligence called machine learning has enabled computers to learn without explicit programming. Driven by the huge improvements in computer processing, a subset of machine learning based on multiple layers of artificial neural networks has been developed to tackle increasingly complex problems. The so-called deep learning applications teach themselves to perform a specific task by automatically extracting essential features from the raw data. Despite the possibilities of automatic feature extraction, most applications of deep learning to power quality are still the same as expert systems or earliest machine learning tools. Moreover, most of the applications are based on synthetic generated data and supervised techniques. In this context, the main motivation of this thesis is providing a new tool based on unsupervised deep learning to handle analytics of time-varying power quality big data. 

The unsupervised deep learning method proposed in this thesis combines a deep autoencoder with clustering for extracting patterns and anomalies in power quality big data. The deep autoencoder maps the original data to a compressed format that contains the principal features of the data. Automatic results are provided by the deep learning, and inferences can be obtained without requiring prior knowledge of deep learning. The outputs from unsupervised deep learning can serve as a guide for further data analysis, highlighting important time steps within large power quality datasets. By following these indications from the deep learning results, experts gain valuable insights into power quality phenomena, which can be referred to as "learning from deep learning". The interpretation of the deep learning results in this thesis allowed to making proper inferences for patterns and anomalies. For power quality measurements synchronised with 24-h, the results allowed making inferences concerning daily variations, seasonality, and the origins of power quality disturbances. For power quality measurements non-synchronised with 24-h, the results could be interpreted visually through the distribution of the patterns in a physical variable, such as the dynamic operating conditions of an electrical railway power system.

An important contribution of this thesis concerns the physical interpretation of the phenomena is related to the anomalies in harmonics caused by geomagnetically induced currents. An interesting finding by applying the deep anomaly detection to measurements in the Swedish transmission grid is the damping of the anomalies caused by geomagnetically induced currents in the winter due to the heating load. This thesis also demonstrated that the signatures for anomalies in harmonic measurements in a Swedish transmission location are similar to the ones found in a low-latitude transmission location at the South Atlantic Anomaly due to geomagnetically induced currents. Moreover, by cross-checking the anomalies at the South Atlantic Anomaly with protection trips with undetermined causes, this thesis demonstrated that anomaly harmonics due to geomagnetically induced currents can cause protection mal trips.

This thesis demonstrates that unsupervised deep learning can serve as an additional tool for compressing time-varying power quality big data into a more interpretable form. Despite the application of an unsupervised method, power quality experts remain significant in power quality studies. The main conclusion of this thesis is that unsupervised deep learning enhances the understanding of power quality experts and provides a complementary approach for analysing and extracting insights from time-varying power quality big data.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
power quality, power system harmonics, electric power distribution, interharmonics, big data analytics, pattern analysis, unsupervised learning, deep learning, artificial intelligence, geomagnetically induced currents
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-100718 (URN)978-91-8048-353-7 (ISBN)978-91-8048-354-4 (ISBN)
Public defence
2023-10-04, Hörsal A, Luleå tekniska universitet, Skellefteå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-08-25 Created: 2023-08-24 Last updated: 2023-09-13Bibliographically approved
Oliveira, R. A. & Bollen, M. H. .. (2023). Deep learning for power quality. Electric power systems research, 214, Article ID 108887.
Open this publication in new window or tab >>Deep learning for power quality
2023 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 214, article id 108887Article, review/survey (Refereed) Published
Abstract [en]

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in terms of application, type of data, and learning technique. This work shows that the majority of the deep learning applications to power quality are based on unrealistic synthetic data and supervised learning without proper labelling. Some applications with deep learning have already been solved by previous machine learning methods or expert systems. The main barriers to implementing deep learning to power quality are related to lack of novelty, low transparency of the deep learning methods, and lack of benchmark databases. This work also discusses that even with automatic feature extraction by deep learning methods, power quality expert knowledge is still needed to implement and analyse the results. The main research gaps identified in this work are related to the applications of semi-supervised learning, explainable deep learning and hybrid approaches combining deep learning with expert systems. Suggestions for overcoming the present limitations are: providing a stronger collaboration among the grid stakeholders and academy to keep track of power quality events; proper labelling and enlarging of datasets in deep learning methods; explaining the end-to-end decision making of deep learning methods; providing open-access databases for comparison purposes.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Artificial intelligence, Data analysis, Deep learning, Machine learning, Power quality
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93726 (URN)10.1016/j.epsr.2022.108887 (DOI)001024999300001 ()2-s2.0-85139840780 (Scopus ID)
Funder
Swedish Energy Agency
Note

Validerad;2022;Nivå 2;2022-10-26 (hanlid)

Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2024-03-07Bibliographically approved
A. Oliveira, R., S. Salles, R. & Rönnberg, S. K. (2023). Deep Learning for Power Quality with Special Reference to Unsupervised Learning. In: 27th International Conference on Electricity Distribution (CIRED 2023): . Paper presented at 27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023 (pp. 935-939). IEEE, Article ID 10417.
Open this publication in new window or tab >>Deep Learning for Power Quality with Special Reference to Unsupervised Learning
2023 (English)In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 935-939, article id 10417Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-100717 (URN)10.1049/icp.2023.0593 (DOI)2-s2.0-85181538517 (Scopus ID)
Conference
27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023
Note

ISBN for host publication: 978-1-83953-855-1;

Available from: 2023-08-24 Created: 2023-08-24 Last updated: 2024-03-11Bibliographically approved
de Oliveira, R. A., Nakhodchi, N., De Souza Salles, R. & Rönnberg, S. K. (2023). Deep Learning Graphical Tool Inspired by Correlation Matrix for Reporting Long-Term Power Quality Data at Multiple Locations of an MV/LV Distribution Grid. In: 27th International Conference on Electricity Distribution (CIRED 2023): . Paper presented at 27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023 (pp. 609-613). IEEE, Article ID 10324.
Open this publication in new window or tab >>Deep Learning Graphical Tool Inspired by Correlation Matrix for Reporting Long-Term Power Quality Data at Multiple Locations of an MV/LV Distribution Grid
2023 (English)In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 609-613, article id 10324Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-103655 (URN)10.1049/icp.2023.0433 (DOI)2-s2.0-85181541951 (Scopus ID)
Conference
27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023
Note

ISBN for host publication: 978-1-83953-855-1

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-02-08Bibliographically approved
Malfoy, A., A. Oliveira, R. & Rönnberg, S. K. (2023). Harmonics in the Transmission and Distribution Grid and their Relation to Geomagnetically Induced Currents. In: 27th International Conference on Electricity Distribution (CIRED 2023): . Paper presented at 27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023 (pp. 60-64). IEEE, Article ID 00121.
Open this publication in new window or tab >>Harmonics in the Transmission and Distribution Grid and their Relation to Geomagnetically Induced Currents
2023 (English)In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 60-64, article id 00121Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Astronomy, Astrophysics and Cosmology
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-103646 (URN)10.1049/icp.2023.0241 (DOI)2-s2.0-85181532521 (Scopus ID)
Conference
27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023
Note

ISBN for host publication: 978-1-83953-855-1; 

Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-03-11Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-5845-5620

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