10.14489/td.2020.02.pp.040-045

2020, 02 February

DOI: 10.14489/td.2020.02.pp.040-045

Babokin G. I., Shprekher D. M., Kolesnikov E. B.
NEURAL NETWORKS AS A MEANS TO PREDICTING THE STATE OF ISOLATION IN NETWORKS WITH ISOLATED NEUTRAL
(pp. 40-45)

Abstract. Electrical insulation is an important parameter of the structural element of electrical equipment, provides reliability and durability of electrical networks with an isolated neutral and safe use of electricity in underground conditions. This article solves the problem of predicting changes in insulation resistance of electrical equipment, taking into account changes in external factors such as humidity and air temperature. An example of using a conventional neural network such as MLP did not give an acceptable result. To solve this problem, a recurrent neural network with longterm memory type LSTM was proposed. Its structure was developed. The simulation results showed that the neural network LSTM successfully coped with the task of predicting changes in insulation resistance, taking into account changing environmental factors. The prediction error for the one-hour interval was 0.052. The proposed idea of continuous monitoring of the insulation resistance with the prediction of its changes for a certain period will avoid sudden failures of electrical components, electric shock and fire caused by a decrease in insulation resistance below the critical value. The forecast of insulation resistance will allow in cases of delay of actuation of the device leakage at high rate of change of the insulation resistance, allows you to increase the efficiency of mining equipment through the reduction of damage from sudden shutdowns of the equipment underground mining machines in the course of their normal work.

Keywords: data mining, artificial neural network, insulation resistance, forecasting, environmental factors, deep neural network, forecast error.

+ - About the Authors Click to collapse

G. I. Babokin (National Research Technological University “MISIS”, Mining Institute, Moscow, Russia)
D. M. Shpreher (Tula State University, Tula, Russia)
E. B. Kolesnikov (Novomoskovsk Institute of Russian Chemical Technology University n. D. I. Mendeleev, Novomoskovsk, Russia)

+ - References Click to collapse

1. Shkrabets F. P. (2010). Monitoring and control of distribution network isolation parameters. Nauchniy vestnik NGU – natsional'niy gorniy universitet, (7 – 8), pp. 84 – 88. Ukraine. [in Russian language]
2. Samoylovich I. S. (1976). Neutral modes of quarry electrical networks. Moscow: Nedra. [in Russian language]
3. Shchutskoy V. I. (Ed.) (1983). Electrical safety in open pit mining. Moscow: Nedra. [in Russian language]
4. Grebchenko N. V., Sidorenko A. A. (2006). Intelligent system for determining the location and extent of local insulation defects in a network with isolated neutral. Relay protection and automation of power systems, pp. 150 – 152. [in Russian language]
5. Bulychev A. V., Nudel'man G. S. (2009). Proactive Relay Protection Functions. Modern directions of development of relay protection systems and automation of power systems, pp. 72 – 78. Moscow. [in Russian language]
6. Utegulov B. B., Uahitova A. B., Begentaev B. M. (2010). Development of measures to improve electrical safety in networks with voltages up to 1000 V of the Kapitalnaya mine of Maykainzoloto JSC. Nauka i tekhnika Kazahstana, (3), pp. 130 – 136. [in Russian language]
7. Sarkisyan S. A. (Ed.), Kaspin V. I., Lisichkin V. A. et al. (1977). Theory of Forecasting and Decision Making. Moscow: Vysshaya shkola. [in Russian language]
8. Shprekher D. M. (2016). Diagnosis and control of the state of the electromechanical systems of mining machines using artificial neural networks. Tambov. [in Russian language]
9. Lyubimova T. V., Gorelova A. V. (2015). The solution to the problem of forecasting using neural networks. Innovatsionnaya nauka, (4), pp. 39 – 42. [in Russian language]
10. Rutkovskaya D., Pilin'skiy M., Rutkovskiy L. (2008). Neural networks, genetic algorithms and fuzzy systems. Moscow: Goryachaya liniya – Telekom. [in Russian language]
11. Haykin S. (2006). Neural networks: a full course. 2nd ed. Moscow: Vil'yams. [in Russian language]

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