CHOOSING A NEURAL NETWORK STRUCTURE FOR SIGNAL PROCESSING AS AN EXPERIMENT PLANNING

Leonid A. Slavutskii, Elena V. Slavutskaya

DOI: 10.47026/1810-1909-2021-3-123-132

 Key words

artificial neural networks, choice of neural network structure, experiment planning, signal processing in electrical engineering, neuro algorithm accuracy estimation.

Abstract

The paper is devoted to the use of artificial neural networks for signal processing in electrical engineering and electric power industry. Direct propagation neural network (perceptron) is considered as an object in the theory of experiment planning. The variants of the neural network structure empirical choice, the quality criteria of its training and testing are analyzed. It is shown that the perceptron structure choice, the training sample, and the training algorithms require planning. Variables and parameters of neuro algorithm that can act as factors, state parameters, and disturbing influences in the framework of the experimental planning theory are discussed. The proposed approach is demonstrated by the example of neural network analysis of the industrial frequency signal of 50 Hz nonlinear distortions. The possibility of using an elementary perceptron with one hidden layer and a minimum number of neurons to correct the transformer saturation current is analyzed. The conditions under which the neuro algorithm allows one to restore the values of the main harmonic amplitude, frequency and phase with an error of no more than one percent are revealed. The signal processing in a “sliding window” with a duration of a fraction of the fundamental frequency period is proposed, and the neuro algorithm accuracy characteristics are estimated. The possibility to automate the neural network structure choosing for signal processing is discussed.

References

1. Korol E.G. Analiz metodov modelirovaniya magnitnykh kharakteristik elektromagnitov dlya kompensatsii magnitnogo polya elektrooborudovaniya [Analysis of methods for modeling the magnetic characteristics of electromagnets for compensating the magnetic field of electrical equipment]. Elektrotekhnika i Elektromekhanika, 2007, no. 2, pp. 31–34.
2. Kruglov V.V., Borisov V.V. Iskusstvennye neironnye seti. Teoriya i praktika [Neural networks. Theory and practice] Moscow, Goryachaya liniya Telekom Publ., 2001, 382 p.
3. Kuzhekov S.L., Nudel’man G.S. Obespecheniye pravil’noy raboty mikroprotsessornykh ustroystv differentsial’noy zashchity pri nasyshchenii transformatorov toka [Ensuring the correct operation of microprocessor devices of differential protection during saturation of current transformers]. Elektromekhanika, 2009, no. 4, pp. 12–17.
4. Lyamets Yu.Ya., Nudel’man G.S., Pavlov A.O., Efimov E.B., Zakon’shek Ya. Raspoznavaemost’ povrezhdenii elektroperedachi, ch. 1,2,3 [Detectability of power transmission damage]. Elektrichestvo, 2001, no. 2, pp. 16–23; no. 3, pp. 16–24; no. 12, pp. 9–22.
5. Slavutskaya E., Slavutskiy L. On choosing the artificial neural networks structure and the algorithms for psycho diagnostic data analyzing [O vybore struktury iskusstvennykh neyrosetey i algoritmov analiza psikhodiagnosticheskikh dannykh]. Kazan pedagogical journal, 2020, no. 5(142), рp. 202–210. DOI:34772/KPJ.2020.142.5.026.
6. Slavutskiy A.L. Accounting the residual magnetization in the transformer for the modeling of transients [Uchet ostatochnoy namagnichennosti v transformatore pri modelirovanii perekhodnykh protsessov]. Vestnik Chuvashskogo universiteta, 2015, no. 1, pp. 122–130.
7. Suchkov V.O., Yadarova O.N., Slavutskii L.A. Distantsionnyi ul’trazvukovoi kontrol’ vozdushnogo potoka na osnove iskusstvennoi neironnoi seti [Remote ultrasonic airflow control based on artificial neural network]. Vestnik Chuvashskogo universiteta, 2015, no. 1, pp. 207-212.
8. Basodi S., Zhang H., Pan Y. Gradient amplification: An efficient way to train deep neural networks. Big Data Mining and Analytics, 2020, vol. 3(3), pp. 196–207.
9. Bhattacharya B. Sinha А. Intelligent Fault Analysis in Electrical Power Grids. In: IEEE 29^th International Conference on Tools with Artificial Intelligence (ICTAI). Boston, 2017, pp. 985–990. DOI: 10.1109/ICTAI.2017.00151.
10. Burton B., Harley R.G. Reducing the computational demands of continually online-trained artificial neural networks for system identification and control of fast processes. IEEE Transactions on Industry Applications, 1998, vol. 34(3), pp. 589–596.
11. Coury D.V., Oleskovicz M., Aggarwal R.K. An ANN routine for fault detection, classification and location in transmission lines. Electrical Power Components and Systems, 2002, no. 30, pp. 1137–1149.
12. Dharmendra K., Moushmi K., Zadgaonkar A.S. Analysis of generated harmonics due to transformer load on power system using artificial neural network. International journal of electrical engineering, 2013, vol. 4(1), pp. 81–90.
13. Grossberg S. A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action. Neurorobot, 2020, vol. 14. DOI: https://doi.org/10.3389/fnbot.2020.00036.
14. Hassan S.R., Rehman A., Shabbir N., Unbreen A. Comparative Analysis of Power Quality Monitoring Systems. NFC-IEFR Journal of Engineering and Scientific Research, 2020. DOI: 10.24081/nijesr.2019.1.0004.
15. He Z., Lin S., Deng Y., Li X., Qian Q. A rough membership neural network approach for fault classification in transmission lines. International Journal of Electrical Power and Energy Systems, 2014, vol. 61, pp. 429–439.
16. Jain A., Thoke A.S., Patel R.N. Fault classification of double circuit transmission line using artificial neural network. International Journal of Electrical Systems Science and Engineering, WASET, USA, 2008, vol. 1, pp. 230–235.
17. Jamil M., Kalam A., Ansari A.Q., Rizwan M. Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line. Applied Soft Computing, 2014, vol. 19, pp. 322–332.
18. Keerthipala W.W.L., Low Tah Chong, Tham Chong Leong. Artificial neural network model for analysis of power system harmonics. IEEE International Conference on Neural Networks, 1995, vol. 2, pp. 905–910.
19. Kulikov A.L., Loskutov A.A., Mitrovic M. Improvement of the technical excellence of multiparameter relay protection by combining the signals of the measuring fault detectors using artificial intelligence methods. International Scientific and Technical Conference Smart Energy Systems 2019 (SES-2019), 2019, vol. 124. DOI: https://doi.org/10.1051/e3sconf/201912401039.
20. Laruhin A., Nikandrov M., Slavutskii L. Anomalous modes recognizing secondary equipment in electric power industry: adaptive neuro algorithms. In: 2019 Int. Ural Conf. on Electrical Power Engineering: Proc. URALCON, 2019, pp. 399–403. DOI: 10.1109/URALCON.2019.8877613.
21. Mahanty R.N., Dutta Gupta P.B. Comparison of fault classification methods based on wavelet analysis and ANN. Electric Power Components and Systems, 2006, vol. 34, pp. 47–60.
22. Mazumdar J., Harley R.G., Lambert F., Venayagamoorthy G. Neural Network Based Method for Predicting Nonlinear Load Harmonics. Power Electronics, IEEE Transactions, 2007, vol. 22(3), pp. 1036–1045. DOI: 10.1109/TPEL.2007.897109.
23. Milanovic J., Ball R.F, Howe W., Preece R., Bollen M. H. J., Elphick S., Cukalevski N. International Industry Practice on Power-Quality Monitoring. IEEE Transactions on Power Delivery, 2014, vol. 29, pp. 934–941.
24. Montgomery D.C. Design and Analysis of Experiments. John Wiley & Sons, Inc. 1997, p. 757.
25. Niekerk C.R., Rens A.P.J., Hoffman A.J. Identification of types of distortion sources in power systems by applying neural networks. 6^th IEEE Africon, 2002, vol. 2(2), pp. 829–834.
26. Osowski S. Neural network for estimation of harmonic components in a power system. IEEЕ Proceedings on Generation, Transmission and Distribution, 1992, vol. 139(2), pp. 129–135.
27. Rosenblatt F. Principles of neurodymamics. Washington, Spartan Books, 1962.
28. Silva K.M., Souza B.A., Brito N.S.D. Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Transactions on Power Delivery, 2006, vol. 21, pp. 2058–2063.
29. Soldatov A.V. Naumov V.A., Antonov V.I., Aleksandrova M.I. Information bases of algorithms for protecting a generator operating on busbars from single-phase-to-ground Faults. Investigation of the Information Bases of Algorithms Controlling Higher Current Harmonics. Power Technology and Engineering, 2019, vol. 53(4), pp. 496–502. DOI: 1007/s10749-019-01105-w.
30. Sundaravaradan N. A., Surya S. Application Technique For Model-Based Approach To Estimate Fault Location. IET Smart Grid, 2019. DOI: 10.1049/iet-stg.2019.0135.

Information about the author

Leonid A. Slavutskii – Doctor of Physical and Mathematical Sciences, Professor, Department of Automation and Control in Technical Systems, Chuvash State University, Russia, Cheboksary (lenya@slavutskii.ru; ORCID: https://orcid.org/0000-0001-6783-2985).

Elena V. Slavutskaya – Doctor of Psychological Sciences, Professor, Department of Psychology and Social Pedagogy, I.Ya. Yakovlev Chuvash State Pedagogical University, Russia, Cheboksary (slavutskayaev@gmail.com; ORCID: https://orcid.org/0000-0002-3759-6288).

For citations

Slavutskii L.A., Slavutskaya E.V. CHOOSING A NEURAL NETWORK STRUCTURE FOR SIGNAL PROCESSING AS AN EXPERIMENT PLANNING. Vestnik Chuvashskogo universiteta, 2021, no. 3, pp. 123–132. DOI: 10.47026/1810-1909-2021-3-123-132 (in Russian).

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