| Full text | |
| Author(s): |
Moraes, Anderson L.
;
Fernandes, Ricardo A. S.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM); v. N/A, p. 5-pg., 2020-01-01. |
| Abstract | |
Currently, research focused on the pattern classification in the area of Power Quality has been using Deep Learning techniques. However, some techniques, such as Convolutional Neural Networks, despite its good performance, require images as input data. In this sense, the present paper proposes an analysis of Recurrence Plots textures as a way to identify Power Quality disturbances. Among the disturbances were considered the short and long duration voltage variations, the oscillatory and impulsive transients, the voltage fluctuations and waveform distortions. These analyzes allowed to identify distinct patterns between disturbances and, consequently, demonstrate that Recurrence Plot can be properly employed as a novel feature engineering technique in the scope of Power Quality disturbances classification. (AU) | |
| FAPESP's process: | 19/15192-9 - Nonintrusive load monitoring based on recurrence plots and deep learning in the context of smart homes |
| Grantee: | Ricardo Augusto Souza Fernandes |
| Support Opportunities: | Regular Research Grants |