A Survey of Prognostics and Health Management for Transformers: From Statistical Analysis to Condition-Based Diagnostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Jiaxiang Cheng Sungin Cho Yap Peng Tan Guoqiang Hu

Abstract

Power transformers are one of the key network components for reliable and efficient operation of power grids. Over the past few decades, there have been growing research efforts in improving the prognostics and health management (PHM) for transformers, including failure analysis using time-to-event data and condition-based diagnostics for both single and multiple components. In this paper, we survey recent literature and relevant works, focusing on widely used statistical models and advanced diagnostic techniques that leverage on condition data and maintenance history. Additionally, we examine the role of artificial intelligence (AI) applications in PHM for power transformers. Finally, we summarize the current limitations and future opportunities to support new research efforts for improving the monitoring of power transformers.

Abstract 160 | PDF Downloads 186

##plugins.themes.bootstrap3.article.details##

Keywords

Power transformer, Prognostics and health management, Statistical analysis, Condition-based diagnostic, Artificial intelligence

References
Aljohani, O., & Abu-Siada, A. (2014). Application of FRA polar plot technique to diagnose internal faults in power transformers. In 2014 IEEE PES general meeting | conference & exposition. IEEE.

Aljohani, O., & Abu-Siada, A. (2016). Application of digital image processing to detect short-circuit turns in power transformers using frequency response analysis. IEEE Transactions on Industrial Informatics, 12(6), 2062– 2073.

Aljohani, O., & Abu-Siada, A. (2017). Application of digital image processing to detect transformer bushing faults and oil degradation using FRA polar plot signature. IEEE Transactions on Dielectrics and Electrical Insulation, 24(1), 428–436.

A. M. Sarhan, J. A. (2013). Exponentiated modified weibull extension distribution. Reliability Engineering & System Safety, 112, 137–144.

Chmura, L., Morshuis, P. H. F., Gulski, E., Smit, J. J., & Janssen, A. (2011). Statistical analysis of subcomponent failures in power transformers. In 2011 IEEE electrical insulation conference (EIC). IEEE.

Cota-Felix, J., Rivas-Davalos, F., & Maximov, S. (2009). A new method to evaluate mean life of power system equipment. In IET conference publications. IET.

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.

Duval, M. (1989). Dissolved gas analysis: It can save your transformer. IEEE Electrical Insulation Magazine, 5(6), 22–27.

Duval, M. (2008). The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers. IEEE Electrical Insulation Magazine, 24(6), 22–29.

Hong, Y., Meeker, W. Q., & McCalley, D. J. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. The Annals of Applied Statistics, 3(2), 857–879.

IEEE PES Transformers Committee, et al. (1992). IEEE guide for the interpretation of gases generated in oilimmersed transformers. IEEE Standards Board.

IEEE PES Transformers Committee, et al. (2019). IEEE guide for the interpretation of gases generated in mineral oil-immersed transformers. IEEE Standards Board, 1–179.

Jiang, R. (2013). A new bathtub curve model with a finite support. Reliability Engineering & System Safety, 119, 44–51.

Johnson, F., & Iliev, K. (2012). Earthquake effects on SDG&e's 500/230kv imperial valley substation. In 2012 IEEE power & energy society general meeting. IEEE.

Jurgensen, J. H., Nordstrom, L., & Hilber, P. (2019). Estimation of individual failure rates for power system components based on risk functions. IEEE Transactions on Power Delivery, 34(4), 1599–1607.

Jurgensen, J. H., Nordstr ¨ om, L., & Hilber, P. (2016). Indi- ¨ vidual failure rates for transformers within a population based on diagnostic measures. Electric Power Systems Research, 141, 354–362.

Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

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, 20(2), 685–690.

Koch, M., & Kruger, M. (2012). A new method for online monitoring of bushings and partial discharges of power transformers. In 2012 IEEE international conference on condition monitoring and diagnosis (pp. 1205–1208). Bali, Indonesia: IEEE.

Lin, J., Su, L., Yan, Y., Sheng, G., Xie, D., & Jiang, X. (2018). Prediction method for power transformer running state based on LSTM DBN network. Energies, 11(7), 1880.

Lu, S. X., Lin, G., que, H., Li, M. J. J., Wei, C. H., & Wang, J. K. (2018). Grey relational analysis using gaussian process regression method for dissolved gas concentration prediction. International Journal of Machine Learning and Cybernetics, 10(6), 1313–1322.

Mariprasath, T., & Kirubakaran, V. (2018). A real time study on condition monitoring of distribution transformer using thermal imager. Infrared Physics & Technology, 90, 78–86.

Martin, D., Marks, J., Saha, T., Krause, O., Russell, G., & Alibegovic-Memisevic, A. (2017). On the development of power transformer failure models: An australian case study. In 2017 IEEE power & energy society general meeting. IEEE.

Martin, D., Marks, J., Saha, T. K., Krause, O., & Mahmoudi, N. (2018). Investigation into modeling australian power transformer failure and retirement statistics. IEEE Transactions on Power Delivery, 33(4), 2011–2019.

Murugan, R., & Ramasamy, R. (2015). Failure analysis of power transformer for effective maintenance planning in electric utilities. Engineering Failure Analysis, 55, 182–192.

Murugan, R., & Ramasamy, R. (2019). Understanding the power transformer component failures for health indexbased maintenance planning in electric utilities. Engineering Failure Analysis, 96, 274–288.

Singh, J., Singh, S., & Singh, A. (2019). Distribution transformer failure modes, effects and criticality analysis (fmeca). Engineering Failure Analysis, 99, 180–191.

Stih, Z., & Mikulecky, A. (2013). Influence of temperature, moisture content and ageing on oil impregnated paper bushings insulation. IEEE Transactions on Dielectrics and Electrical Insulation, 20(4), 1421–1427.

Tenbohlen, S., Jagers, J., Gebauer, J., Muller, P., Lapworth, J., Yukiyasu, S., . . . Bo, L. (2012). Transformer reliability survey: Interim report. Cigre Electra, 46–49.

Vahidi, F., & Tenbohlen, S. (2014). Statistical failure analysis of european substation transformers. ETGFachbericht-Diagnostik elektrischer Betriebsmittel, 1– 5.

Weibull, W. (1961). Fatigue testing and analysis of results. Bockamollan , Brosarps Station, Sweden: Pergamon

Yasid, N. F. M., Yousof, M. F. M., Rahman, R. A., Zainuddin, H., & Ghani, S. A. (2019). The effect of short circuit fault on one winding to other windings in FRA. International Journal of Power Electronics and Drive Systems (IJPEDS), 10(2), 585.

Zeng, B., Guo, J., Zhang, F., Zhu, W., Xiao, Z., Huang, S., & Fan, P. (2020). Prediction model for dissolved gas concentration in transformer oil based on modified grey wolf optimizer and LSSVM with grey relational analysis and empirical mode decomposition. Energies, 13(2), 422.

Zhou, D. (2013). Transformer lifetime modelling based on condition monitoring data. International Journal of Advances in Engineering & Technology, 6(2), 613.

Zhou, D., Wang, Z., Jarman, P., & Li, C. (2014). Data requisites for transformer statistical lifetime modelling—part II: Combination of random and agingrelated failures. IEEE Transactions on Power Delivery, 29(1), 154–160.
Section
Regular Session Papers