Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring

Authors

  • Ihor Reva Kremenchuk Mykhailo Ostrohradskyi National University, Ukraine
  • Oleh Todorov Kremenchuk Mykhailo Ostrohradskyi national University, Ukraine
  • Maksim Bezzub Kremenchuk Mykhailo Ostrohradskyi national University, Ukraine

DOI:

https://doi.org/10.15588/1607-6761-2022-1-2

Keywords:

transformer monitoring, non-invasive monitoring, neural network, identification of parameters of the substitution scheme, T is a similar transformer replacement scheme

Abstract

Purpose. Application of a specially constructed neural network with the task of identifying the parameter substitution scheme in non-invasive monitoring conditions.

Methodology. Use of electrical measurement methods by non-invasive monitoring, methods of identification and training of neural networks based on anterior and back propagation error, NARX networks.

Findings. The power transformer is an important object of the power system of the electric shop substation. At the same time, frequent transitions from underload to partial overload mode are possible, which creates preconditions for the development of undesirable phenomena in transformers. Monitoring of the power transformer based on control of its substitution scheme, allows to pass to control of its basic parameters irrespective of an operating mode. Non-invasive monitoring works well in the context of the task, because research shows that the parameters of voltage and current, which it operates well reflect the dependence on changes in the parameters of the substitution scheme. Based on T equivalent the substitution scheme can detect and predict changes in parameters substitution schemes according to the parameters of the current and voltage regime flowing in this system. Many neural networks will work out the calculation of electrical and electrotechnical  equivalent circuits as a task of identifying the parameters of electrical circuits in static conditions. In the process of identification, the ability of neural networks of different designs to identify one of the substitution scheme parameters, to resist damage was tested, which corrected the value of an unknown parameter. The experiment made it possible to obtain data for comparing the effectiveness of various architectures of neural networks in relation to the real parameters of the equivalent circuit. Concretizing the obtained results, we say that the NARX architecture is able to identify parameters in standard modes for all elements of the substitution circuit, which further opens up opportunities for its improvement in the calculation of nonlinear elements of the transformer when operating in a saturated state.

Originality. It is established that the neural boundaries studied in the work are able to determine the parameters of the replacement circuit of a transformer or electric machine in static operating modes, which allows in the future to monitor the state of windings and magnetic circuit according to their values.

Practical value. Using a neural network in the monitoring system allows you to get clear values of the equivalent circuit parameters, regardless of the mode, the proposed method significantly reduces the amount of time spent on monitoring the transformer parameters, allows you to control the power level, and, if necessary, reduce the amount of information required for the transformer monitoring.

Author Biographies

Ihor Reva, Kremenchuk Mykhailo Ostrohradskyi National University

PhD student, Department of electricity consumption system and power management of the Kremenchuk Mykhailo Ostrohradskyi national university, Kremenchuk

Oleh Todorov, Kremenchuk Mykhailo Ostrohradskyi national University

PhD student, Department of electricity consumption system and power management of the Kremenchuk Mykhailo Ostrohradskyi national university, Kremenchuk

Maksim Bezzub, Kremenchuk Mykhailo Ostrohradskyi national University

PhD student, Department of electricity consumption system and power management of the Kremenchuk Mykhailo Ostrohradskyi national university, Kremenchuk

References

Montana, Johny & Candelo-Becerra, John & Racines, Diana. (2018). Non-Intrusive Electrical Load Moni-toring System Applying Neural Networks with Com-bined Steady-State Electrical Variables. Tehnicki Vjesnik. 25. 1321-1329. 10.17559/TV-20170317203817.

Mohanty, A. R. (2015). Machinery condition monitor-ing: principles and practices. http://www.crcnetbase.com/isbn/9781466593053.

Li, Zongbo, Zaibin Jiao, and Anyang He. "Knowledge-based Artificial Neural Network for Power Trans-former Protection." Iet Generation, Transmission & Distribution. 14.24 (2020): 5782-5791. Print.

D. Reeve and B. Barton Low Voltage Monitoring ,Primer and Guideline,October 2020,p113, https://www.ena.org.nz/resources/publications/document/805

G. M. V. Zambrano, A. C. Ferreira and L. P. Caloba, "Power transformer equivalent circuit identification by artificial neural network using frequency response analysis," 2006 IEEE Power Engineering Society General Meeting, 2006, pp. 6 pp.-, doi: 10.1109/PES.2006.1708931.

Minin, Alexey & Chistyakov, Yury & Kholodova, E. & Zimmermann, Hans & Knoll, A.. (2012). Complex-valued open recurrent neural network for power transformer modeling. Int. J. Appl. Math. Inform. Is-sue 1, Volume 6, 2012 6. pp41-48.

Kuczmann, M., Szьcs, A., Kovбcs, G. "Transformer Model Identification by Ārtap: A Benchmark Prob-lem", Periodica Polytechnica Electrical Engineering and Computer Science, 65(2), pp. 123–130, 2021. https://doi.org/10.3311/PPee.17606

Abdelaziz, Almoataz Y, Aleem S. H. E. Abdel, and Anamika Yadav. Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection. , 2022. Internet resource.

M. I. Abdelwanis, A. Abaza, R. A. El-Sehiemy, M. N. Ibrahim and H. Rezk, "Parameter Estimation of Electric Power Transformers Using Coyote Optimiza-tion Algorithm With Experimental Verification," in IEEE Access, vol. 8, pp. 50036-50044, 2020, doi: 10.1109/ACCESS.2020.2978398..

C. A. Arenas-Acuña, J. A. Rodriguez-Contreras, O. D. Montoya, and E. Rivas-Trujillo, “Black-Hole Opti-mization Applied to the Parametric Estimation in Dis-tribution Transformers Considering Voltage and Cur-rent Measures,” Computers, vol. 10, no. 10, p. 124, Oct. 2021 [Online]. Available: http://dx.doi.org/10.3390/computers10100124

Z O. Çetin, A. Dalcalı, F. Temurtaş,A comparative study on parameters estimation of squirrel cage in-duction motors using neural networks with unmemo-rized training,Engineering Science and Technology, an International Journal,Volume 23, Issue 5,2020 ,p.1126-1133,doi.org/10.1016/j.jestch.2020.03.011.

Andrejevic Stosovic, Miona & Litovski, Vanco. (2003). Electronic circuit modeling using artificial neural network. Journal of Automatic Control. 13. 10.2298/JAC0301031A.

Jin, Z. and Kaba, S. (2021) Deep Neural Network Based Behavioral Model of Nonlinear Circuits. Jour-nal of Applied Mathematics and Physics, 9, 403-412. doi: 10.4236/jamp.2021.93028.

G. Stegmayer, "Volterra series and neural networks to model an electronic device nonlinear behavior," 2004 IEEE International Joint Conference on Neural Net-works (IEEE Cat. No.04CH37541), 2004, pp. 2907-2910 vol.4, doi: 10.1109/IJCNN.2004.1381123.

K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural net-works," in IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4-27, March 1990, doi: 10.1109/72.80202.

Ramírez J. et al. (2020) Power Transformer Forecast-ing in Smart Grids Using NARX Neural Networks. In: Valenzuela O., Rojas F., Herrera L.J., Pomares H., Rojas I. (eds) Theory and Applications of Time Se-ries Analysis. ITISE 2019. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-56219-9_26

Scales, L. E. Introduction to non-linear optimization / L.E. Scales Springer-Verlag New York 1985

Published

2022-03-30

How to Cite

Reva, I., Todorov, O., & Bezzub, M. (2022). Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring . Electrical Engineering and Power Engineering, (1), 19–29. https://doi.org/10.15588/1607-6761-2022-1-2