Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA
One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This typ...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3598 |
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| author | Jose R. Huerta-Rosales David Granados-Lieberman Arturo Garcia-Perez David Camarena-Martinez Juan P. Amezquita-Sanchez Martin Valtierra-Rodriguez |
| author_facet | Jose R. Huerta-Rosales David Granados-Lieberman Arturo Garcia-Perez David Camarena-Martinez Juan P. Amezquita-Sanchez Martin Valtierra-Rodriguez |
| author_sort | Jose R. Huerta-Rosales |
| collection | DOAJ |
| description | One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained. |
| first_indexed | 2024-03-10T11:10:23Z |
| format | Article |
| id | doaj.art-7e525beec1de49b9b7d541b5e5941d52 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:10:23Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7e525beec1de49b9b7d541b5e5941d522023-11-21T20:50:24ZengMDPI AGSensors1424-82202021-05-012111359810.3390/s21113598Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGAJose R. Huerta-Rosales0David Granados-Lieberman1Arturo Garcia-Perez2David Camarena-Martinez3Juan P. Amezquita-Sanchez4Martin Valtierra-Rodriguez5ENAP-Research Group, CA-Sistemas Dinámicos y Control, Laboratorio de Sistemas y Equipos Eléctricos (LaSEE), Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, CP 76807, MexicoENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energía Eléctrica, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México, Instituto Tecnológico Superior de Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, Colonia El Copal, Irapuato, Guanajuato, CP 36821, MexicoENAP-Research Group, División de Ingeniería, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca, Guanajuato, CP 36885, MexicoENAP-Research Group, División de Ingeniería, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca, Guanajuato, CP 36885, MexicoENAP-Research Group, CA-Sistemas Dinámicos y Control, Laboratorio de Sistemas y Equipos Eléctricos (LaSEE), Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, CP 76807, MexicoENAP-Research Group, CA-Sistemas Dinámicos y Control, Laboratorio de Sistemas y Equipos Eléctricos (LaSEE), Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, CP 76807, MexicoOne of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.https://www.mdpi.com/1424-8220/21/11/3598fault diagnosissupport vector machinelinear discriminant analysisFPGAshort-circuit faulttransformer |
| spellingShingle | Jose R. Huerta-Rosales David Granados-Lieberman Arturo Garcia-Perez David Camarena-Martinez Juan P. Amezquita-Sanchez Martin Valtierra-Rodriguez Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA Sensors fault diagnosis support vector machine linear discriminant analysis FPGA short-circuit fault transformer |
| title | Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA |
| title_full | Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA |
| title_fullStr | Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA |
| title_full_unstemmed | Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA |
| title_short | Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA |
| title_sort | short circuited turn fault diagnosis in transformers by using vibration signals statistical time features and support vector machines on fpga |
| topic | fault diagnosis support vector machine linear discriminant analysis FPGA short-circuit fault transformer |
| url | https://www.mdpi.com/1424-8220/21/11/3598 |
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