Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Eduardo Perez-Anaya, Arturo Yosimar Jaen-Cuellar, David Alejandro Elvira-Ortiz, Rene de Jesus Romero-Troncoso and Juan Jose Saucedo-Dorantes ()
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Eduardo Perez-Anaya: Engineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, Mexico
Arturo Yosimar Jaen-Cuellar: Academic Center of Advanced and Sustainable Technology (CATAS), Autonomous University of Queretaro, Tequisquiapan Campus, Carretera No. 120, San Juan del Río-Xilitla, Km 19+500, Tequisquiapan 76750, QRO, Mexico
David Alejandro Elvira-Ortiz: Academic Center of Advanced and Sustainable Technology (CATAS), Autonomous University of Queretaro, Tequisquiapan Campus, Carretera No. 120, San Juan del Río-Xilitla, Km 19+500, Tequisquiapan 76750, QRO, Mexico
Rene de Jesus Romero-Troncoso: Engineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, Mexico
Juan Jose Saucedo-Dorantes: Engineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, Mexico

Energies, 2024, vol. 17, issue 4, 1-17

Abstract: Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

Keywords: power quality disturbance; condition monitoring; continuous wavelet transform; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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