Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals
Source abstract
Abstract
Transformers are vital elements in electrical networks, but they are prone to various faults throughout their service life. Among these, a winding short-circuit fault is of particular concern to researchers, as it is a crucial and vulnerable component of the transformers. Therefore, if this fault is not addressed at an early stage, it can increase costs for users and affect industrial processes as well as other electrical machines. In recent years, the analysis of vibration signals has emerged as one of the most promising solutions for detecting faults in transformers. Nonetheless, it is not a straightforward process because of the nonstationary properties of the vibration signals and their high-level noise, as well as their different features when the transformer operates under different conditions. Based on the previously mentioned points, the motivation of this work is to contribute a methodology that can detect different severities of short-circuited turns (SCTs) in transformers in both transient and steady-state operating regimes using vibration signals. The proposed approach consists of a wavelet-based denoising stage, a short-time Fourier transform (STFT)-based analysis stage for the transient state, a Fourier transform (FT)-based analysis stage for the steady-state, the application of two fault indicators, i.e., the energy index and the total harmonic distortion index, and two neural networks for automatic diagnosis. To evaluate the effectiveness of the proposed methodology, a modified transformer is used to experimentally reproduce different levels of SCTs, i.e., 0-healthy, 5, 10, 15, 20, 25, and 30 SCTs, in a controlled way. The obtained results show that the proposed approach can detect the fault condition, starting from an initial stage for consolidation and a severe stage to accurately assess the fault severity, achieving accuracy values of 90%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power Transformer Diagnostics and Insulation · Machine Fault Diagnosis Techniques · Power Quality and Harmonics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ENAP-RG, CA-Fuentes Alternas y Calidad de la Energía Eléctrica, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México/ITS de Irapuato, Carretera Irapuato-Silao km 12.5, Colonia El Copal, Irapuato 36821, Mexico
ENAP-RG, CA Sistemas Dinámicos y Control, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico
ENAP-RG, División de Ingeniería, Universidad de Guanajuato (UG), Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico
Funding
Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT)—México · Sistema Nacional de Investigadoras e Investigadores (SNII)–CONAHCYT–México
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