Estimation of the switching losses in DC-DC boost converters by various machine learning methods

Abstract

DC-DC converter circuits are topologies commonly used in power electronics applications such as renewable energy sources, electric vehicles, uninterruptible power supplies and DC transmission systems. The most important factors affecting efficiency and thus performance is the choice of the power semiconductor switching element as well as the circuit design and types of these topologies. In this context, power semiconductors are determined according to the switching frequency and current-voltage parameters. However, due to other operating modes of the circuit and load variation during the power conversion, the losses of the switching elements do not remain constant. In this study, a parametric simulation is performed in a conventional DC-DC boost converter circuit using the parameters related to the Insulated-Gate Bipolar Transistor (IGBT) power-switching element selected at a certain current-voltage capacity. These parameters are switching frequency, duty ratio and load change of the converter. Finally, using the data obtained, the loss of switching losses are estimated by the Multilayer Perceptron (MLP), Support Vector Machine (SVM), K- Nearest Neighbors (KNN) and Random Forest (RF) Machine Learning (ML) techniques.

Keywords

• K- Nearest Neighbors,Multilayer Perceptron,Random Forests,Support Vector Machine,Switching Losses

References

1. [1] Uğurlu A, Gokcol C. A case study of PV-Wind-Diesel-Battery hybrid system, Journal of Energy Systems 2017, 1(4),138-147 DOI: 10.30521/jes.348335.
2. [2] Ali, A., Erçelebi, E., A Low-cost modelling of the variable frequency drive optimum in industrial applications. Journal of Energy Systems 2018, 2(1), 28-42, DOI: 10.30521/jes.405774.
3. [3] Shahin, A, Payman, A, Martin, JP, Pierfederici, S, Meibody-Tabar, F. Approximate novel loss formulae estimation for optimization of power controller of dc/dc converter. 36th Annual Conference on IEEE Industrial Electronics Society (IECON), 7-10 November 2010, IEEE, Glendale, AZ, USA, pp. 373-378, DOI: 10.1109/IECON.2010.5674999.
4. [4] John Shen, Z, Xiong, Y, Cheng, X, Fu, Y, Kumar, P. Power MOSFET switching loss analysis: A New insight. 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting, 8-12 October 2006, IEEE, Tampa, FL, USA, pp. 1438-1442, DOI: 10.1109/IAS.2006.256719.
5. [5] Rajapakse, AD, Gole, AM, Wilson, PL. Approximate loss formulae for estimation of IGBT switching losses through EMTP type simulations. The International Conference on Power Systems Transients (IPST), 19-23 June 2005, Montreal, Canada, pp. 184.
6. [6] Dupé, V, Jammes, B, Séguier, L, Alonso, C. Accurate power loss model of a boost cell in a multiphase converter for phase management. 16th European Conference on Power Electronics and Applications, 26-28 August 2014, IEEE, Lappeenranta, Finland, pp. 1-9, DOI: 10.1109/EPE.2014.6910935.
7. [7] Dupé, V, Jammes, B, Alonso, C, Séguier, L. Behavioral modeling of power losses in FSBB converters, PCIM Europe, May 2013, Nuremberg, Germany, pp. 1701-1706.
8. [8] Allard, B, Morel, H, Ammous, K, Xuefang, LS, Bergogne, D, Brevet, O. Application of averaged models to real-time monitoring of power converters. 32nd Annual Power Electronics Specialists Conference (IEEE Cat. No.01CH37230), 17-21 June 2001, IEEE, Vancouver, Canada, pp. 486-491, DOI: 10.1109/PESC.2001.954161.

9. [9] Krismer, F, Kolar, JW. Accurate power loss model derivation of a high-current dual active bridge converter for an automotive application. IEEE Transactions on Industrial Electronics 2010, 57, 881-891 DOI: 10.1109/TIE.2009.2025284.
10. [10] Eroğlu, Y, Ulusam Seçkiner, S. Early fault prediction of a wind turbine using a novel ANN training algorithm based on ant colony optimization. Journal of Energy Systems 2019, 3, 139-147. DOI: 10.30521/jes.613315.
11. [11] Samadianfard, S, Jarhan, S, Sadri Nahand, H. Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation. Journal of Energy Systems 2018, 2, 180-189. DOI: 10.30521/jes.458328.
12. [12] Bulut, EB, Cengiz, K. Determination the effects of duty cycle and switching frequency on efficiency of boost converter for fixed load applications. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM) 2017, 1, 69-75
13. [13] Kouro, S, Perez, M, Robles, H, Rodriguez, J. Switching loss analysis of modulation methods used in cascaded h-bridge multilevel converters. IEEE Power Electronics Specialists Conference, 15-19 June 2008, IEEE, Rhodes, Greece, pp. 4662-4668, DOI: 10.1109/PESC.2008.4592703.
14. [14] Balci, S, Kayabasi, A, Yildiz, B. Artificial neural network-based estimation of the output ripple of the DC-DC boost battery charger for EVs. 6th Eur. Conf. Ren. Energy Sys. 25-27 June 2018, Istanbul, Turkey.
15. [15] Rashid, MH. Power Electronics Devices, Circuits, And Applications Fourth Edition. 2004, NJ, USA, Pearson Education.
16. [16] M. F. Aslan, K. Sabanci, and A. Durdu. Different wheat species classifier application of ANN and ELM. Journal of Multidisciplinary Engineering Science and Technology 2017, 4, 8194-8198.
17. [17] Haykin S. Neural networks: A Comprehensive Foundation, 1994, New York, USA, Macmillan College Publishing Company.
18. [18] Sabanci, K, Kayabasi, A, Toktas, A. Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture 2017, 97, 2588-2593, DOI: 10.1002/jsfa.8080
19. [19] Kayabasi, A. An Application of ANN trained by ABC algorithm for classification of wheat grains. International Journal of Intelligent Systems and Applications in Engineering 2018, 6, 85-91.
20. [20] Gupta, DK. A review on wireless sensor networks. Network and Complex Systems 2013, 3, 18-23.
21. [21] Kshirsagar, P, Rathod, N. Artificial neural network. International Journal of Computer Applications 2012, 2, 12-16.
22. [22] Bhatia, N, Vandana. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security 2010, 8, 302-305.
23. [23] Sabanci, K, Koklu, M. The classification of eye state by using kNN and MLP classification models according to the EEG signals. International Journal of Intelligent Systems and Applications in Engineering 2015, 3, 127-130, DOI: 10.18201/ijisae.75836.
24. [24] Ben-Hur, A, Weston, J. A user’s guide to support vector machines. Data mining techniques for the life sciences 2010, 223-239, DOI: 10.1007/978-1-60327-241-4_13.
25. [25] Akar, Ö, Güngör, O. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 2012, 1,105-112, DOI: 10.9733/jgg.241212.1.
26. [26] Uestuen, B., Melssen, W.J., Buydens, L.M.C. Facilitating the application of Support Vector Regression by using a universal Pearson VII function-based kernel. Chemometrics and Intelligent Laboratory Systems 2006, 81, 29-40, DOI: 10.1016/j.chemolab.2005.09.003.