|
Fault detection and condition assessment of power transformers using practical signal processing methods
|
Morteza Saeid1   , Hamed Zeinoddini-Meymand * 1, Davoud Abootorabi zarchi2 |
1- Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran.
2- Department of Electrical Engineering, Yazd University, Yazd, Iran. |
|
|
Abstract: (2999 Views) |
Dissolved gases in transformer oil indicate the occurrence of a fault in the transformer. Many standards use the ratio of dissolved gases in transformer oil to detect faults in transformers. These traditional methods cannot, however, be used in cases in which the transformer needs to be immediately removed from service in case of a serious error such as electric arcs to prevent the error from spreading. For this purpose, this paper uses signal processing methods that analyze the signal online. The paper assumes that CO and CO2 dissolved gases in transformer oil are measured online by a sensor and then some signal processing methods are applied to the measurement data. These methods include Fast Fourier Transform, Discrete Wavelet Transform, Hilbert Transform, Gabor Transform, the combination of discrete wavelet and Hilbert transform, the combination of discrete wavelet and Gabor transform, and spectrogram. These methods are continuously applied to these two gases that are soluble in transformer oil to determine their variations in the transformer oil at a certain time or a certain frequency. The gases are also modified and then the performance of each of the processing methods mentioned in these changes is investigated. Fault detection reference is the CIGER 761-2019 standard. The purpose of this paper is to find out the samples of gases change in a frequency interval or timeframe together irrespective of the faults in the transformer and changes in the volume of dissolved gases in transformer oil, analyze the signal processing methods, and detect the type of fault using CIGRE 761-2019 standard. The fast Fourier transform method analyzes signal power by frequency. The discrete wavelet transform method extracts high-frequency components of gases and detects faults based on the largest components. The Hilbert transform method converts the signal into two real and imaginary parts. Then, it uses the imaginary part that represents the signal phase to detect faults. The Gabor transform method extracts instantaneous frequencies in the time-frequency plane and uses this method to detect faults. In the methods that combine discrete wavelet transform and Hilbert or Gabor transform, high-frequency components are extracted by discrete wavelet transform, and then Hilbert or Gabor transform methods are applied. The spectrogram method also indicates the size of the short-time Fourier transform, which is used to analyze the signal. These signal processing methods are compared in several features, and the discrete wavelet transform method is introduced as the best method for fault detection.
|
|
| Keywords: Power Transformer, Fault detection, Discrete Wavelet Transform, Hilbert Transform, Fast Fourier Transform, Gabor Transform, Spectrogram |
|
|
Full-Text [PDF 1694 kb]
(883 Downloads)
|
Type of Study: Research |
Received: 2021/09/3 | Accepted: 2021/12/5 | Published: 2022/04/26
|
|
|
|
|
|
|
| References |
|
1. CIGRE A2.49, (2019). Condition Assessment of Power Transformers, Technical Brochure CIGRE, No. 761. 2. Khan, S.A., Equbal, M.D., Islam, T., (2015). A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 22, No. 1, pp. 590-596. [ DOI:10.1109/TDEI.2014.004478] 3. Hooshmand, R.A., Parastegari, M., Forghani, Z., (2012). Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers, IEEE Electrical Insulation Magazine, Vol. 28, No. 5, pp. 32-42. [ DOI:10.1109/MEI.2012.6268440] 4. Khan, S.A., Equbal, M.D., Islam, T., (2014). ANFIS based identification and location of paper insulation faults of an oil immersed transformer, 6th IEEE Power India International Conference, pp. 1-6. [ DOI:10.1109/34084POWERI.2014.7117715] 5. Cui, Y., Ma, H., Saha, T., (2014). Improvement of power transformer insulation diagnosis using oil characteristics data preprocessed by SMOTEBoost technique, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 21, No. 5, pp. 2363-2373. [ DOI:10.1109/TDEI.2014.004547] 6. Kim, S.W., Kim, S.J., Seo, H.D., Jung, J.R., Yang, H.J., Duval, M., (2013). New methods of DGA diagnosis using IEC TC 10 and related databases Part 1: application of gas-ratio combinations, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 20, No. 2, pp. 685-690. [ DOI:10.1109/TDEI.2013.6508773] 7. Duraisamy, V., Devarajan, N., Somasundareswari, D., Vasanth, A.A.M., Sivanandam, S.N., (2007). Neuro fuzzy schemes for fault detection in power transformer, Applied Soft Computing, Vol. 7, No. 2, pp. 534-539. [ DOI:10.1016/j.asoc.2006.10.001] 8. Veerasamy, V., Abdul Wahab, N. I., Ramachandran, R., Mansoor, M., Thirumeni, M., Lutfi Othman, M., (2018). High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system, Energies, Vol. 11, No .12, pp. 3330. [ DOI:10.3390/en11123330] 9. Barbosa, F.R., Almeida, O.M., Braga, A.P.S., Tavares, C.M., (2009). Artificial Neural Network Application in Estimation of Dissolved Gases in Insulating Mineral Oil from Physical-Chemical Datas for Incipient Fault Diagnosis, 15th International Conference on Intelligent System Applications to Power Systems, IEEE, pp. 1-5. [ DOI:10.1109/ISAP.2009.5352919] 10. Ushakov, V.Y., Mytnikov, A.V., Lavrinovich, V.A., Lavrinovich, A.V., (2022). Transformer Condition Control: Standardized Technologies of Condition Monitoring for High Voltage, Springer, pp. 23-68. [ DOI:10.1007/978-3-030-83198-1_2] 11. Yang, M.T., Hu, L.S., (2013). Intelligent fault types diagnostic system for dissolved gas analysis of oil-immersed power transformer, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 20, No. 6, pp. 2317-2324. [ DOI:10.1109/TDEI.2013.6678885] 12. Li, A., Yang, X., Dong, H., Xie, Z., Yang, C., (2018). Machine learning-based sensor data modeling methods for power transformer PHM, Sensors, Vol. 18, No .12, pp. 4430. [ DOI:10.3390/s18124430] 13. Duval, M., (2002). A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Magazine, Vol. 18, No. 3, pp. 8-17. [ DOI:10.1109/MEI.2002.1014963] 14. Faiz, J., Soleimani, M., (2017). Dissolved gas analysis evaluation in electric power transformers using conventional methods a review, IEEE Transactions Dielectrics Electrical Insulation, Vol. 24, No .2, pp.1239-1248. [ DOI:10.1109/TDEI.2017.005959] 15. Ma, H., Saha, T.K., Ekanayake, C., Martin, D., (2015). Smart transformer for smart grid-intelligent framework and techniques for power transformer asset management, IEEE Transactions on Smart Grid, Vol. 6, No. 2, pp. 1026-1034. [ DOI:10.1109/TSG.2014.2384501] 16. Silva, S., Costa, P., Santana, M., Leite, D., (2020). Evolving neuro-fuzzy network for real-time high impedance fault detection and classification, Neural Computing and Applications, Vol. 32, No .12, pp. 7597-7610. [ DOI:10.1007/s00521-018-3789-2] 17. Zhou, H., Hong, K., Huang, H., Zhou, J., (2016). Transformer winding fault detection by vibration analysis methods, Applied Acoustics, Vol. 114, pp. 136-146. [ DOI:10.1016/j.apacoust.2016.07.024] 18. Hashemnia, N., Abu-Siada, A., Islam, S., (2015). Improved power transformer winding fault detection using FRA diagnostics-part 1: axial displacement simulation, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 22, No. 1, pp. 556-563. [ DOI:10.1109/TDEI.2014.004591] 19. Senobari, R.K., Sadeh, J., Borsi, H., (2018). Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review, Electric Power Systems Research, Vol. 155, pp. 172-183. [ DOI:10.1016/j.epsr.2017.10.014] 20. Yang, X., Chen, W., Li, A., Yang, C., Xie, Z., Dong, H., (2019). BA-PNN-based methods for power transformer fault diagnosis, Advanced engineering informatics, Vol. 39, pp. 178-185. [ DOI:10.1016/j.aei.2019.01.001] 21. Bacha, K., Souahlia, S., Gossa, M., (2012). Power transformer fault diagnosis based on dissolved gas analysis by support vector machine, Electric Power Systems Research, Vol. 83, No .1, pp. 73-79. [ DOI:10.1016/j.epsr.2011.09.012] 22. Zhang, D., Li, C., Shahidehpour, M., Wu, Q., Zhou, B., Zhang, C., Huang, W., (2022). A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability, International Journal of Electrical Power & Energy Systems, Vol. 134, pp. 107356. [ DOI:10.1016/j.ijepes.2021.107356] 23. Taheri, B., Sedighizadeh, M., (2021). A moving window average method for internal fault detection of power transformers, Cleaner Engineering and Technology, Vol. 4, pp. 100195. [ DOI:10.1016/j.clet.2021.100195] 24. Tavakoli, A., De Maria, L., Valecillos, B., Bartalesi, D., Garatti, S., Bittanti, S., (2020). A Machine Learning approach to fault detection in transformers by using vibration data, IFAC-PapersOnLine, Vol. 53, No .2, pp. 13656-13661. [ DOI:10.1016/j.ifacol.2020.12.866] 25. Yu, S., Zhao, D., Chen, W., Hou, H., (2016). Oil-immersed power transformer internal fault diagnosis research based on probabilistic neural network, Procedia Computer Science, Vol. 83, pp. 1327-1331. [ DOI:10.1016/j.procs.2016.04.276] 26. Malik, H., Yadav, A.K., Mishra, S., Mehto, T., (2013). Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformer, International Journal of Electrical Power & Energy Systems, Vol. 53, pp. 256-271. [ DOI:10.1016/j.ijepes.2013.04.023] 27. Senobari, R.K., Sadeh, J., Borsi, H., (2018). Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review, Electric Power Systems Research, Vol. 155, pp. 172-183. [ DOI:10.1016/j.epsr.2017.10.014] 28. Yadaiah, N., Ravi, N., (2011). Internal fault detection techniques for power transformers, Applied Soft Computing, Vol. 11, No .8, 5259-5269. [ DOI:10.1016/j.asoc.2011.05.034] 29. Zakaria, R., Le Ruyet, D., (2016). Theoretical analysis of the power spectral density for FFT-FBMC signals, IEEE Communications Letters, Vol. 20, No. 9, pp. 1748-1751. [ DOI:10.1109/LCOMM.2016.2588497] 30. Atto, A.M., Trouvé, E., Nicolas, J.M., Lê, T.T., (2016). Wavelet operators and multiplicative observation models-Application to sar image time-series analysis, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 11, pp. 6606-6624. [ DOI:10.1109/TGRS.2016.2587626] 31. Prabhakar, S., Mohanty, A.R., Sekhar, A.S., (2002). Application of discrete wavelet transform for detection of ball bearing race faults, Tribology International, Vol. 35, No. 12, pp. 793-800. [ DOI:10.1016/S0301-679X(02)00063-4] 32. Ghods, A., Lee, H.H., (2016). Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors, Neurocomputing, Vol. 188, pp. 206-216. [ DOI:10.1016/j.neucom.2015.06.100] 33. Feldman, M., (2011). Hilbert transform in vibration analysis, Mechanical systems and signal processing, Vol. 25, No. 3, pp. 735-802. [ DOI:10.1016/j.ymssp.2010.07.018] 34. Treml, A.E., Flauzino, R.A., Brito, G.C., (2019). EMD and MCSA improved via Hilbert Transform analysis on asynchronous machines for broken bar detection using vibration analysis, IEEE Milan PowerTech Conf., pp. 1-6. [ DOI:10.1109/PTC.2019.8810643] 35. Thakur, G., Wu, H.T., (2011). Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples, SIAM Journal on Mathematical Analysis, Vol. 43, No. 5, pp. 2078-2095. [ DOI:10.1137/100798818] 36. Wang, S., Chen, X., Selesnick, I. W., Guo, Y., Tong, C., Zhang, X., (2018). Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis, Mechanical Systems and Signal Processing, Vol. 100, pp. 242-288. [ DOI:10.1016/j.ymssp.2017.07.009] 37. Wu, G., Zhou, Y., (2018). Seismic data analysis using synchrosqueezing short time Fourier transform, Journal of Geophysics and Engineering, Vol. 54, No. 4, pp. 1663-1672. [ DOI:10.1088/1742-2140/aabf1d] |
|
Saeid M, Zeinoddini-Meymand H, abootorabi zarchi D. Fault detection and condition assessment of power transformers using practical signal processing methods. ieijqp 2022; 11 (1) :44-56 URL: http://ieijqp.ir/article-1-841-en.html
|
