Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN
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 strateg...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/4/852 |
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| author | Eduardo Perez-Anaya Arturo Yosimar Jaen-Cuellar David Alejandro Elvira-Ortiz Rene de Jesus Romero-Troncoso Juan Jose Saucedo-Dorantes |
| author_facet | Eduardo Perez-Anaya Arturo Yosimar Jaen-Cuellar David Alejandro Elvira-Ortiz Rene de Jesus Romero-Troncoso Juan Jose Saucedo-Dorantes |
| author_sort | Eduardo Perez-Anaya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:34:02Z |
| format | Article |
| id | doaj.art-e258a53713ed40ebb2f5801231c30a07 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:34:02Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e258a53713ed40ebb2f5801231c30a072024-02-23T15:15:15ZengMDPI AGEnergies1996-10732024-02-0117485210.3390/en17040852Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNNEduardo Perez-Anaya0Arturo Yosimar Jaen-Cuellar1David Alejandro Elvira-Ortiz2Rene de Jesus Romero-Troncoso3Juan Jose Saucedo-Dorantes4Engineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, MexicoAcademic 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, MexicoAcademic 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, MexicoEngineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, MexicoEngineering Faculty, Autonomous University of Queretaro, Campus San Juan del Río, Av. Río Moctezuma 249, San Juan del Rio 76807, QRO, MexicoEnergy 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.https://www.mdpi.com/1996-1073/17/4/852power quality disturbancecondition monitoringcontinuous wavelet transformconvolutional neural network |
| spellingShingle | Eduardo Perez-Anaya Arturo Yosimar Jaen-Cuellar David Alejandro Elvira-Ortiz Rene de Jesus Romero-Troncoso Juan Jose Saucedo-Dorantes Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN Energies power quality disturbance condition monitoring continuous wavelet transform convolutional neural network |
| title | Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN |
| title_full | Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN |
| title_fullStr | Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN |
| title_full_unstemmed | Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN |
| title_short | Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN |
| title_sort | methodology for the detection and classification of power quality disturbances using cwt and cnn |
| topic | power quality disturbance condition monitoring continuous wavelet transform convolutional neural network |
| url | https://www.mdpi.com/1996-1073/17/4/852 |
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