Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals
Abstract
:1. Introduction
2. Theoretical Background
2.1. Vibrations in Transformers
2.2. Wavelet Denoising
- Introduce a noisy signal into the process. A noisy signal is described as follows [27]:
- In the next step, the noisy signal is decomposed using the discrete WT (DWT). This method allows for the decomposition of the signal into sets of coefficients at different frequency levels according to the following [30]:
2.3. Short-Time Fourier Transform
2.4. Measurement Functions
2.5. Comb Filter
2.6. Artificial Neural Networks
3. Methodology and Experimental Setup
3.1. Proposed Methodology
3.2. Experimentation
4. Experimentation and Results
4.1. Wavelet Denoising
4.2. Results for the Transient State
4.3. Results for the Steady State
4.4. ANN Results
4.5. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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SCTs | |||||||
---|---|---|---|---|---|---|---|
Signal | 0 | 5 | 10 | 15 | 20 | 25 | 30 |
Vx | 1 | 1.0787 | 1.1394 | 1.3116 | 1.3564 | 1.8395 | 2.7043 |
Vy | 1 | 0.9002 | 1.2071 | 1.2624 | 1.2409 | 1.8031 | 1.8108 |
Vz | 1 | 1.0071 | 1.0095 | 1.0291 | 1.0341 | 1.0795 | 1.0709 |
SCTs | |||||||
---|---|---|---|---|---|---|---|
Signal | 0 | 5 | 10 | 15 | 20 | 25 | 30 |
Vx | 1 | 1.1750 | 1.2670 | 1.5372 | 1.8855 | 2.5642 | 2.7475 |
Vy | 1 | 1.0455 | 1.8513 | 2.4707 | 2.5634 | 2.6960 | 3.3665 |
Vz | 1 | 1.0228 | 1.7007 | 3.1712 | 3.9219 | 5.1645 | 6.0375 |
SCT Class | Accuracy | Recall | Specificity | Precision | F1-Score |
---|---|---|---|---|---|
0 | 0.9000 | 0.9000 | 0.9889 | 0.9310 | 0.9153 |
5 | 0.8333 | 0.8333 | 0.9778 | 0.8621 | 0.8475 |
10 | 0.9333 | 0.9333 | 0.9833 | 0.9032 | 0.9180 |
15 | 0.8333 | 0.8333 | 0.9778 | 0.8621 | 0.8475 |
20 | 0.9000 | 0.9000 | 0.9667 | 0.8182 | 0.8571 |
25 | 0.9333 | 0.9333 | 0.9944 | 0.9655 | 0.9492 |
30 | 0.9667 | 0.9667 | 0.9944 | 0.9667 | 0.9667 |
SCT Class | Accuracy | Recall | Specificity | Precision | F1-Score |
---|---|---|---|---|---|
0 | 0.9333 | 0.9333 | 0.9944 | 0.9655 | 0.9492 |
5 | 0.8667 | 0.8667 | 0.9833 | 0.8966 | 0.8814 |
10 | 0.9000 | 0.9000 | 0.9778 | 0.8710 | 0.8852 |
15 | 0.9333 | 0.9333 | 0.9778 | 0.8750 | 0.9032 |
20 | 0.9000 | 0.9000 | 0.9889 | 0.9310 | 0.9153 |
25 | 0.8667 | 0.8667 | 0.9833 | 0.8966 | 0.8814 |
30 | 0.9333 | 0.9333 | 0.9833 | 0.9032 | 0.9180 |
Reference | Signal Processing Methods | Analyzed State | Number of Sensors Employed | Detected Faults | Severity Levels | Automatic Diagnosis |
---|---|---|---|---|---|---|
Proposal | Wavelet denoising and FT-based methods | Transient and steady | 1 | SCTs | 6 | ANNs |
[3] | Electromagnetic force analysis | Steady | 5 | Winding loosening | 3 | -- |
[13] | Empirical wavelet transform and HT | Transient and steady | 2 | Winding deformation | 3 | -- |
[19] | CEEMDAN and multiscale dispersion entropy | Steady | 1 | Winding and core loosening | 1 | Density peaks clustering |
[20] | VMD and WT | Steady | 1 | Winding deformation | 3 | CNN |
[21] | NMD and HT-based RMS | Transient and steady | 1 | SCTs | 6 | FLS |
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Granados-Lieberman, D.; Huerta-Rosales, J.R.; Gonzalez-Cordoba, J.L.; Amezquita-Sanchez, J.P.; Valtierra-Rodriguez, M.; Camarena-Martinez, D. Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals. Appl. Sci. 2023, 13, 12218. https://doi.org/10.3390/app132212218
Granados-Lieberman D, Huerta-Rosales JR, Gonzalez-Cordoba JL, Amezquita-Sanchez JP, Valtierra-Rodriguez M, Camarena-Martinez D. Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals. Applied Sciences. 2023; 13(22):12218. https://doi.org/10.3390/app132212218
Chicago/Turabian StyleGranados-Lieberman, David, Jose R. Huerta-Rosales, Jose L. Gonzalez-Cordoba, Juan P. Amezquita-Sanchez, Martin Valtierra-Rodriguez, and David Camarena-Martinez. 2023. "Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals" Applied Sciences 13, no. 22: 12218. https://doi.org/10.3390/app132212218
APA StyleGranados-Lieberman, D., Huerta-Rosales, J. R., Gonzalez-Cordoba, J. L., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., & Camarena-Martinez, D. (2023). Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals. Applied Sciences, 13(22), 12218. https://doi.org/10.3390/app132212218