Прогнозирование стоимости электроэнергии и состояния изоляции электрооборудования
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Forecasting the cost of electricity and the state of insulation of electrical equipment

idKulinich Y.M. idShuharev S.A.

UDC 004.9:629.423.1
DOI: 10.26102/2310-6018/2020.30.3.041

  • Abstract
  • List of references
  • About authors

The paper deals with the application of the method of time series analysis to predict the cost of electricity and assess the state of insulation of power circuits of an electric locomotive. The proposed approach makes it possible, on a scientific basis, to plan the amount of funds allocated to pay for electricity, as well as to take timely measures aimed at restoring insulation and excluding the causes of fires that occur on locomotives. Time series analysis was carried out with the help of an application program that allows assessing the trend of changes in the indicators under consideration. A device for monitoring the state of insulation of power circuits of an electric locomotive is also proposed, in which the developed program for forecasting time series is implemented. Installing the device described in the work on the locomotive will allow timely assessing the current and predicted state of insulation, as well as taking timely measures to restore it. The urgency of the problem of diagnosing the state of insulation is due to the aging processes of fixed assets (machine tools and equipment) at industrial enterprises, which requires timely measures to restore the state of insulation of power electrical equipment. The application program is implemented in the MatLab package and is used to predict the cost of electricity. To expand the possibilities of using the application in other applications, the source code of the program was transformed into the code written in the high-level C language. The program obtained in this way is used in the PIC18F452 microcontroller to assess the state of the insulation of the power circuits of an electric locomotive.

1. Kravchuk V.V., Kulinich Yu.M., Ponyavkin D.Yu. Causes of fires on locomotives. Improving the efficiency of railway transport in the Far Eastern region abstracts of the scientific and technical conference. 1995:145. (In Russ)

2. Kravchuk V.V., Kulinich Yu.M., Ponyavkin D.Yu. The fires continue. Locomotive. 1995;8:34-35. (In Russ)

3. Ivanov V.V., Osetrov E.S. Prediction of daily passenger traffic in the Moscow metro. Letters in JINR. 2018;15(1):88-108. (In Russ)

4. Savin A.S., Khokhlov A.A., Chetov A.I. Analysis of time series in the application to the study of customer behavior. Internet journal "SCIENCE". 2015;7(3). Available at: http://naukovedenie.ru/PDF/41TVN315.pdf. (In Russ)

5. J. Harmouche, D. Fourer et al. The Sliding Singular Spectrum Analysis: a Data-Driven Non Stationary Signal Decomposition Tool. IEEE Transactions on Signal Processing. 2018;66(1):251-263.

6. Strizhov V.V. Regression Model Selection Methods. Computing Center them. A.A. Dorodnitsyna Russian Acad. sciences. 2010. (In Russ)

7. Golyandina N.E. Caterpillar-SSA Method: Time Series Forecast: Tutorial. 2004. (In Russ)

8. Varfolomeeva A.A., Fedorova V.P. Local forecasting methods with metric selection. Machine Learning and Data Analysis. 2012;1(3):367-375. (In Russ)

9. Stepanov D.V., Golyandina N.E. Variants of the "Caterpillar"-SSA method for forecasting multidimensional time series. Proceedings of the IV International Conference "System Identification and Management Tasks" SICPRO'05. 2005:1831-1848. (In Russ)

10. I.V. Fadeev, N.P. Ivkin and others. Autoregressive forecasting algorithms. Machine Learning and Data Analysis. 2011;1(1):92-103. (In Russ)

11. Dyakonov V.P. MATLAB 6/6.1/6.5 MATLAB 6 / 6.1 / 6.5 + Simulink 4/5 in mathematics and modeling. Complete user guide. SOLON-Press. 2009. (In Russ)

12. N. Golyandina, E. Osipov. The "Caterpillar"-SSA method for analysis of time series with missing values. Journal of Statistical Planning and Inference. 2007;137(8):2642-2653.

13. Kulinich Yu.M., Malova Yu.G. Device for diagnosing the state of insulation of power circuits. Patent 2590221. Russian Federation. G01R 31/00. Applicant and patent holder «FESTU». stated 20.04.2015. published 10.07.2016.

Kulinich Yuri Mikhailovich
Doctor of Technical Sciences, Professor
Email: kulinitsch@rambler.ru

ORCID |

Federal State Budgetary Educational Institution Of Higher Education "Far Eastern State Transport University"

Khabarovsk, Russian Federation

Shuharev Sergey Anatolevich
Candidate of Technical Sciences
Email: shuharevsa@gmail.com

ORCID |

Federal State Budgetary Educational Institution Of Higher Education "Far Eastern State Transport University"

Khabarovsk, Russian Federation

Keywords: time series forecasting, singular spectral analysis method, singular decomposition, electricity cost, insulation condition

For citation: Kulinich Y.M. Shuharev S.A. Forecasting the cost of electricity and the state of insulation of electrical equipment. Modeling, Optimization and Information Technology. 2020;8(3). Available from: https://moit.vivt.ru/wp-content/uploads/2020/08/KulinichShuharev_3_20_1.pdf DOI: 10.26102/2310-6018/2020.30.3.041 (In Russ).

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