COMPARATIVE ANALYSIS OF NEURAL NETWORK ALGORITHM ACCURACY IN FAULT LOCALIZATION BY THE ONSET MOMENT AND TRANSITION PROCESS PARAMETERS

Igor A. Tabakov, Alexandr L. Slavutskiy, Leonid A. Slavutskii

DOI: 10.47026/1810-1909-2020-3-132-140

Key words

neural network analysis, elementary perceptron, fault localization, transient processes, power system, power transmission line.

Annotation

Fault localization in power lines and other elements of the power system is based on the analysis of transient processes parameters or, for the wave method, on fixation of the transition process onset. Both approaches require modern digital methods of signals analysis and processing. In this paper, the analysis of signals for fault localization is carried out using the simplest artificial neural network based on an elementary perceptron. Training and testing of the neural network are carried out on the example of a sample of signals (1000 to 5000 records) obtained during simulating a short circuit on a power line. Signals that correspond to the short-circuit transition process are determined by two independent random variables: the onset moment of the short circuit (voltage and current phase), and the place of fault. The simulation used a qualitative simplified approach: instead of splitting the power line into many P-sections, resistivity, inductance and power line capacity in one section were considered variable depending on the fault location. The input of the artificial neural network was supplied with voltage counts with a sample rate of 600 Hz standard for measuring organs, and the output, as a target function, was the onset moment or distance to the short circuit site. Comparative analysis of errors in training and testing the artificial neural network for different target functions at its output is carried out. The accuracy of fault localization and the possibility of using the proposed neuroalgorithm are discussed.

References

1. Arzhannikov E.A., Lukoyanov V.Yu., Misrikhanov M.Sh. Redacted by V.A. Shuina Opredelenie mesta korotkogo zamykaniya na vysokovol’tnykh liniyakh elektroperedachi [Detection of short circuit location on high-voltage power lines]. Moscow, Energoatomizdat Publ., 2003, 272 p.
2. Artsishevskii Ya.L., Arslan Zhamsran Metod povysheniya tochnosti opredeleniya mest povrezhdeniya elementov elektricheskoi seti putem utochneniya ee parametrov [The method of improving the accuracy of determining the locations of damage to the electrical network elements by clarifying its parameters]. Vestnik Moskovskogo energeticheskogo instituta, 2007, no. 1, pp. 64–
3. Zakon’shek Ya., Slavutskii A.L. Tsifrovoe modelirovanie sovremennykh energosistem v real’nom vremeni [Digital simulation of real-time power systems]. Releinaya zashchita i avtomatizatsiya, 2012, no.1, 66–72.
4. Kozlov V.N., Bychkov Yu.V., Ermakov K.I. O tochnosti sovremennykh ustroistv OMP [About accuracy of modern devices for network damage location]. Releinaya zashchita i avtomatizatsiya, 2016, no. 1, pp. 42–
5. Koshcheev M.I., Slavutskiy A.L., Slavutskii L.A. Prostyye neyrosetevyye algoritmy dlya volnovogo metoda opredeleniya mesta povrezhdeniya elektroseti [Simple neural network algorithms for the wave method of fault location in power networks].Vestnik Chuvashskogo universiteta, 2019, no.3, pp. 110–118.
6. 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.
7. Kulikov A.L., Petrukhin A.A., Kudryavtsev D.M. Diagnosticheskii kompleks po issle­dova­niyu linii elektroperedach [Diagnostic complex for the study of power lines]. Izvestiya vuzov. Problemy energetiki, 2007, no. 7–8. pp. 17–22.
8. Lachugin V.F., Panfilov D.I., Smirnov A.N. Realizatsiya volnovogo metoda opredeleniya mesta povrezhdeniya na liniyakh elektroperedachi s ispol’zovaniem statisticheskikh metodov analiza dannykh [Implementation of the wave method of determining the location of damage on power lines using statistical data analysis methods]. Izvestiya RAN. Energetik, 2013, no. 6, pp. 137–146.
9. Lyamets Yu.Ya., Belyanin A.A., Voronov P.I. Analiz perekhodnykh protsessov v dlinnoi linii v bazise diskretnogo i nepreryvnogo vremeni [Analysis of transients in a long line in the basis of discrete and continuous time]. Izvestiya vysshikh uchebnykh zavedenii. Elektromekhanika, 2012, no. 4, pp. 11–16.
10. 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. Parts 1–3]. Elektrichestvo [Electricity], 2001, no. 2, pp. 16–23; no. 3, pp. 16–24; no. 12, pp. 9–22.
11. Slavutskiy A.L. Primeneniye algoritma Dommelya dlya modelirovaniya tsepi s poluprovodnikovymi elementami i klyuchami s SHIM upravleniyem [Application of dommel algorithm for simulation of semiconductor circuits with pwm control switches].Vestnik Chuvashskogo universiteta, 2014, 2, pp. 57–65.
12. Shalyt G.M. Opredelenie mest povrezhdeniya v elektricheskikh setyakh [Determination of places of damage in electrical networks]. Moscow, Energoizdat Publ., 1982, 312 p.
13. Bhattacharya B., Sinha A. Intelligent Fault Analysis in Electrical Power Grids. IEEE 29^th International Conference on Tools with Artificial Intelligence (ICTAI). Boston, IEEE, 2017, 985–990. DOI: 10.1109/ICTAI.2017.00151.
14. Bewley L.V. Travelling waves on transmission systems. New York, John Wiley and Sons, 1933, 333 p.
15. Dommel H.W. Digital Computer Solution of Electromagnetic Transients in Single- and Multiphase Networks. IEEE Transactions on Power Apparatus and Systems, 1969, vol. Pas-88, no. 4, pp. 388–399.
16. Elhaffar A.M. Power Transmission Line Fault Location Based on Current Travelling Waves, Doctoral Dissertation. Helsinki University of Technology, Helsinki,
17. Kasztenny B., Guzman A., Mangapathirao V.M., Titiksha J. Locating Faults Before the Breaker Opens -Adaptive Autoreclosing Based on the Location of the Fault. 44^th Annual Western Protective Relay Conference, 2017, 1–15.
18. Lachugin V.F., Panfilov D.I., Smirnov A.N., Obraztsov S.A., Ryvkin A.A., Shimina A.O. A Multifunctional Device for Recording the Monitoring of Electric Power Quality and for Fault Finding on Electric Transmission Lines. Power technology and engineering, 2014, vol. 47, no. 5, pp. 386–392.
19. Laruhin A., Nikandrov M., Slavutskii L. Anomalous modes recognizing secondary equipment in electric power industry: adaptive neuro algorithms. In: 2019 International Ural conference on electrical power engineering, Proceedings URALCON 2019, pp. 399–403.
20. Malathi V., Marimuthu N.S. Wavelet Transform and Support Vector Machine Approach for Fault Location in Power Transmission Line. World Academy of Science, Engineering and Technology, 39, 2010.
21. Rosenblatt F. Principles of neurodymamics. Washington, D.C., Spartan books, 1962.
22. Saha M.M, Izykowski J., Rosolowski E. Fault Location in Power Networks. 1^st New York, Springer-Verlag, 2010.
23. Slavutskaya E.V., Abrukov V.S., Slavutskii L.A. Simple neuro network algorithms for evaluating latent links of younger adolescent’s psychological characteristics. Experimental Psychology, 2019, vol. 12, no. 2, pp. 131–142.
24. Swagata Das, Surya Santoso, Anish Gaikwad, Mahendra Patel. Impedance-Based Fault Location in Transmission Networks: Theory and Application. IEEE Access, vol. 2, New York, 2009.
25. Thomas D.W.P., Carvalho R.J.O., Pereira E.T. Fault Location in Distribution Systems Based on Traveling Waves. IEEE Bologna PowerTech Conference. Bologna, Italy,
26. Wang J., Liu X., Pan Z. A New Fault Location Method for Distribution Network Based on Traveling Wave Theory. Advanced Materials Research, 2015, vols. 1070–1072, pp. 718–725.

Download the full article