Power Quality Disturbances Segmentation: An Approach Based on Gramian Angular Field

Abstract

The advanced metering infrastructure of smart grids has impacted the increase in the volume of data acquired. Therefore, the elimination of non-useful data is important to reduce the storage and processing load/cost in the utility’s control and operations center. Thus, in the management of data related to power quality, it is important to perform the segmentation of disturbances present in oscillographic records, being this a non-trivial task. In this sense, the present article proposes an adaptive approach for disturbance segmentation based on image analysis. Computational experiments were performed involving different disturbances, acquired at 512 samples/cycle, convolved with random noise. In order to demonstrate the robustness of the proposed approach, lower sampling rates were evaluated and compared. Thus, the main contribution of this article is the proposition of a novel approach for automatic and adaptive segmentation of different power quality disturbances, including single/mixed and fast events, under low sampling rates and the presence of random noise. From the results, a high precision was reached for disturbances with noise level up to 35 dB, presenting an absolute mean error range between 0.045 and 8.812 ms for signals at 512 samples/cycle and between 0.195 and 11.431 ms for signals at 64 samples/cycle.

Keywords:

• Gramian angular field
• fuzzy inference system
• power distribution systems
• power quality disturbances
• signal segmentation

Subject classification codes:

• power quality
• distribution systems
• digital signal processor
• artificial intelligence
• fuzzy logic

ACKNOWLEDGMENTS

This work was supported in part by the São Paulo Research Foundation (FAPESP) [Grant Numbers 2019/15192-9; 2021/04872-9; 2023/00182-3] and the National Council for Scientific and Technological Development (CNPq) [Grant Number 406453/2021-7]. The authors would also like to thank the University of São Paulo and the Federal University of São Carlos, São Carlos, Brazil for the facilities provided.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Anderson Luis de Moraes

Anderson Luis de Moraes was born in Descalvado, Brazil, in 1992. He received a B.Sc. degree in Electrical Engineering from the Central Paulista University Center, São Carlos, in 2015, and M.Sc. degree in Electrical Engineering from the Federal University of São Carlos, São Carlos, Brazil, in 2021. He is currently a Ph.D. student in Electrical Engineering at the University of São Paulo, São Carlos, Brazil. His research interests include the application of machine learning for power quality and fault location in the context of smart grids and microgrids.

Denis Vinicius Coury

Denis Vinicius Coury was born in Brazil. He received a B.Sc. degree in Electrical Engineering from the Federal University of Uberlândia, Brazil in 1983, an M.Sc. degree in Electrical Engineering from São Carlos School of Engineering, University of São Paulo, Brazil in 1986, and a Ph.D. degree from Bath University, Bath, U.K. in 1992. He worked for the Technological Research Institute, São Paulo, Brazil, from 1985 to 1986. He joined the Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil in 1986, where he is currently a Full Professor with the Power Systems Group. He spent his Sabbatical at Cornell University, Ithaca, NY, USA from 1999 to 2000. His areas of research interest are power system protection, expert systems, smart grids, and artificial neural networks.

Ricardo Augusto Souza Fernandes

Ricardo Augusto Souza Fernandes was born in Barretos, Brazil, in 1984. He received a B.Sc. degree in Electrical Engineering from the Educational Foundation of Barretos, Barretos in 2006, and M.Sc. and Ph.D. degrees in Electrical Engineering from the University of São Paulo, São Carlos, Brazil in 2009 and 2011, respectively. In 2015 and 2017, he was a Visiting Professor at the Polytechnic Institute of Porto, Portugal. He is currently an Associate Professor at the Federal University of São Carlos, São Carlos, Brazil. His research interests include the application of machine learning for fault location, power quality and demand response in the context of smart grids and microgrids.