Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions

Jose R. Huerta-Rosales, David Granados-Lieberman, Juan P. Amezquita-Sanchez, Arturo Garcia-Perez, Maximiliano Bueno-Lopez and Martin Valtierra-Rodriguez ()
Additional contact information
Jose R. Huerta-Rosales: ENAP-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 76807, Querétaro, Mexico
David Granados-Lieberman: ENAP-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), Carretera Irapuato-Silao km 12.5, Colonia El Copal, Irapuato 36821, Guanajuato, Mexico
Juan P. Amezquita-Sanchez: ENAP-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 76807, Querétaro, Mexico
Arturo Garcia-Perez: ENAP-Research Group, División de Ingenierías, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico
Maximiliano Bueno-Lopez: Department of Electronics, Instrumentation, and Control, Universidad del Cauca, Popayán 190002, Cauca, Colombia
Martin Valtierra-Rodriguez: ENAP-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 76807, Querétaro, Mexico

Energies, 2022, vol. 15, issue 22, 1-15

Abstract: The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used.

Keywords: artificial neural networks; contrast estimation; fault diagnosis; short-circuited turns; transformer fault; vibroacoustic signals (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/22/8508/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/22/8508/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:22:p:8508-:d:972412

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().