Reliability Assessment of Power Electronic Converters Using Replicator Neural Networks

Document Type : Original Article

Authors
Reza Amjadi Fard 1
Farhad Bagheroskouei 2
1 Satelite Research Institute
2 Satellite Research Institute (SRI) of Iranian Space Research Center (ISRC)
10.22034/jssta.2023.348730.1088
Abstract
Reliability assessment of power converters is extremely important due to the degradation of the converter performance under the thermal and electrical stresses. The normal or abnormal operation of a converter is determined based on the quality of the manufacturing process and the environmental and operating conditions. The failure indices are based on the previous failures data which are calculated using the history of the main parameter of the converter which are strongly affected by the aging process. In this article, a new real-time indicator is introduced using the monitoring of the main parameters of the converter. Each indicator is modeled using Replicator Neural Network (RNN) and the network reconstruction coefficient or reconstruction error will be considered as the reliability index or the coefficient of anomaly of the converter. In fact, the reliability assessment is based on the comparison between a reference model of the converter in normal conditions and the estimation of abnormal operation of the converter in the future. In the proposed method, a normal distribution function on the reconstructed error signal, their fit and percentage distance are introduced as the abnormality risk coefficient. The advantages of this method include taking into account all the uncertainties in the process of Manufacturing the power switch and its working conditions, not needing the aging test process in preparing the failure data and taking into account all the failures
Keywords
Remaining useful lifetime estimation#Semiconductor devices# Neural Network# Failure diagnostic
[1] R. Alizadeh and H. Alan Mantooth, "A Review of Architectural Design and System Compatibility of Power Modules and Their Impacts on Power Electronics Systems," in IEEE Transactions on Power Electronics, vol. 36, no. 10, pp. 11631-11646, Oct. 2021.
[2] S. Peyghami, P. Palensky and F. Blaabjerg, "An Overview on the Reliability of Modern Power Electronic Based Power Systems," IEEE Open Journal of Power Electronics, vol. 1, pp. 34-50, 2020.
[3] S. Peyghami, Z. Wang and F. Blaabjerg, "A Guideline for Reliability Prediction in Power Electronic Converters," IEEE Transactions on Power Electronics, vol. 35, no. 10, pp. 10958-10968, Oct. 2020.
[4] J. Harikumaran et al., "Failure Modes and Reliability Oriented System Design for Aerospace Power Electronic Converters," IEEE Open Journal of the Industrial Electronics Society, vol. 2, pp. 53-64, 2021.
[5] B. Wang, J. Cai, X. Du and L. Zhou, "Review of power semiconductor device reliability for power converters," CPSS Transactions on Power Electronics and Applications, vol. 2, no. 2, pp. 101-117, 2017.
[6] Y. Luo, F. Xiao, B. Wang and B. Liu, "Failure analysis of power electronic devices and their applications under extreme conditions," Chinese Journal of Electrical Engineering, vol. 2, no. 1, pp. 91-100, June 2016.
[7] V.S.B. Kurukuru, A. Haque, R. Kumar, M.A. Khan and A.K. Tripathy, "Machine Learning based Fault Classification Approach for Power electronic converters," 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020.
[8] K. Fischer et al., "Field-Experience Based Root-Cause Analysis of Power-Converter Failure in Wind Turbines," in IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2481-2492, May 2015.
[9] S. Peyghami, F. Blaabjerg and P. Palensky, "Incorporating Power Electronic Converters Reliability into Modern Power System Reliability Analysis," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 1668-1681, April 2021.
[10] D. Ronanki and S. S. Williamson, "Failure Prediction of Submodule Capacitors in Modular Multilevel Converter by Monitoring the Intrinsic Capacitor Voltage Fluctuations," IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 2585-2594, April 2020.
[11] Y. Chen, H. Wu, M. Chou and K. Lee, "Online Failure Prediction of the Electrolytic Capacitor for LC Filter of Switching-Mode Power Converters," IEEE Transactions on Industrial Electronics, vol. 55, no. 1, pp. 400-406, Jan. 2008.
[12] Dau, Hoang Anh, Vic Ciesielski, and Andy Song. "Anomaly detection using replicator neural networks trained on examples of one class." Asia-Pacific Conference on Simulated Evolution and Learning. Springer, Cham, 2014.
[13] Hawkins, Simon, et al. "Outlier detection using replicator neural networks." International Conference on Data Warehousing and Knowledge Discovery. Springer, Berlin, Heidelberg, 2002.
[14] S. Peyghami, Z. Wang and F. Blaabjerg, "A Guideline for Reliability Prediction in Power Electronic Converters," IEEE Transactions on Power Electronics, vol. 35, no. 10, pp. 10958-10968, Oct. 2020.
[15] J. Harikumaran et al., "Failure Modes and Reliability Oriented System Design for Aerospace Power Electronic Converters," IEEE Open Journal of the Industrial Electronics Society, vol. 2, pp. 53-64, 2021.
[16] H. Wang et al., "Transitioning to Physics-of-Failure as a Reliability Driver in Power Electronics," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, no. 1, pp. 97-114, March 2014.
[17] H. Wang, J. Przybilla, H. Zhang and J. Schiele, "A new press pack IGBT for high reliable applications with short circuit failure mode," CPSS Transactions on Power Electronics and Applications, vol. 6, no. 2, pp. 107-114, June 2021.
[18] A. Abuelnaga, M. Narimani and A. S. Bahman, "A Review on IGBT Module Failure Modes and Lifetime Testing," IEEE Access, vol. 9, pp. 9643-9663, 2021.
[19] H. Oh, B. Han, P. McCluskey, C. Han and B. D. Youn, "Physics-of-Failure, Condition Monitoring, and Prognostics of Insulated Gate Bipolar Transistor Modules: A Review," IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2413-2426, May 2015.
[20] V. Smet et al., "Ageing and Failure Modes of IGBT Modules in High-Temperature Power Cycling," IEEE Transactions on Industrial Electronics, vol. 58, no. 10, pp. 4931-4941, Oct. 2011.
[21] U. Choi, F. Blaabjerg and K. Lee, "Study and Handling Methods of Power IGBT Module Failures in Power Electronic Converter Systems," IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2517-2533, May 2015.
[22] T.J. Lee, et al. "Greenhouse: A zero-positive machine learning system for time-series anomaly detection." arXiv 2018, arXiv: 1801.03168.
[23] B.F. Lammers, “Replicator Neural Networks for Anomaly Detection,” Ph.D. Thesis, School of Economic, Erasmus University, Rotterdam, Netherland, 2018.
[24] G. Pang, C. Shen, L. Cao and A.V. D. Hengel, “Deep learning for anomaly detection: A review,” ACM Computing Surveys, vol. 54. No. 2, pp. 1-38, March 2021.
[25] S. Thudumu, P. Branch, J. Jin and J. Singh, “A comprehensive survey of anomaly detection techniques for high dimensional big data,” Journal of Big Data, vol. 7, pp. 1-30, 2020.

  • Receive Date 26 June 2022
  • Revise Date 21 January 2023
  • Accept Date 20 February 2023