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- Salles, Rafael S., et al.
(författare)
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Observations on Harmonics Summation in Transmission Systems: Alternative Aggregation Estimation
- 2022
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Ingår i: International Transactions on Electrical Energy Systems. - : Hindawi Publishing Corporation. - 2050-7038.
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Tidskriftsartikel (refereegranskat)abstract
- 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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- De Souza Salles, Rafael, et al.
(författare)
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Classification of Scalogram Signatures for Power Quality Disturbances Using Transfer Learning
- 2022
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Ingår i: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings. - : IEEE. - 9781665416399
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Konferensbidrag (refereegranskat)abstract
- 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.
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