ginal article Application of a microprocessor-based relay protection unit for identifying internal faults of electrical equipment of electrical installations

We aim to determine the applicability of standard microprocessor units of relay protection and automatics for obtaining a digital signal of currents for their subsequent mathematical processing with the purpose of identifying internal faults of electrical equipment. Mathematical processing of experimental data (time series) based on regression analysis approximation in orthogonal basis was carried out. To that end, the weight coefficients of basis functions obtained by the least squares method were compared in terms of multidimensional space vectors, corresponding to the coordinates of this space. The investigated signal was a data set obtained by field experiments conducted with an induction motor, which assumed the possibility of creating artificial internal damage. Experimental data were obtained using two devices with different sampling rates and quantization levels. The first set of data was obtained using a 12-bit PCI board of analog-to-digital converter for installation in a National Instruments 6024E PC at a sampling rate of 10 kHz. The second set of data was obtained using a standard block of microprocessor relay protection and automation at a sampling rate of 2.4 kHz. Indicators for the presence of internal damage to the rotor circuit of an induction motor, which reduces the motor energy characteristics without affecting its operation significantly, were determined. The indicators of the damaged and undamaged state differed by a factor of five. The proposed method for selecting the diagnostic sign of internal damage of electrical equipment of electrical installations detects a 3% deviation in their parameters from the normal state, ignoring the presence of electrical/mechanical load. The diagnostic sign was established to behave similarly in the presence of internal damage, in both sets of signals under study. Thus, the possibility of obtaining a digital signal of acceptable accuracy from standard relay protection units for ensuring reliable identification of internal faults of electrical equipment of electrical installations is confirmed.

1. Litvinov S.N., Lebedev V.D., Gusenkov A.V. Analysis of methods for monitoring the technical condition of highvoltage electronic measuring transformers. iPolytech Journal. 2023;27(2):322-338. (In Russ.). https://doi.org/10.21285/1814-3520-2023-2-322-338. EDN: TQSDVV.

2. Chisedzi L.Р., Muteba M. Detection of broken rotor bars in cage induction motors using machine learning methods. Sensors. 2023;23(22):9079-9087. https://doi.org/10.3390/s23229079.

3. Zhou Guangyang, Zhang Xiahui, Han Minxiao, Filizadeh S., Geng Zhi. Single-ended fault detection scheme using support vector machine for multi-terminal direct current systems based on modular multilevel converter. Journal of Modern Power Systems and Clean Energy. 2023;11(3):990-1000. https://doi.org/10.35833/MPCE.2021.000404. EDN: DKBSJP.

4. Bahgat B.H., Elhay E.А., Sutikno T., Elkholy M.M. Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors. International Journal of Power Electronics and Drive Systems. 2024;15(3):1968-1989. https://doi.org/10.11591/ijpeds.v15.i3.pp1968-1989.

5. Bechiri M.B., Allal A., Naoui М., Khechekhouche A., Alsaif H. Effective diagnosis approach for broken rotor bar fault using Bayesian-based optimization of machine learning hyper parameters. IEEE Access. 2024;12(6):139923- 139936. https://doi.org/10.1109/ACCESS.2024.3464108.

6. Kumar R.S., Indragandhi, V., Aljafari, B., Kareri, T. Rotor bar fault diagnosis in indirect field-oriented control-fed induction motor drive using Hilbert transform, discrete wavelet transform, and energy eigenvalue computation. Machines. 2023;11(7):711. https://doi.org/11.711.10.3390/machines11070711.

7. Ghanbari T., Mehraban A. Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park’s vectors. IET Electric Power Applications. 2023;18(7):356- 366. https://doi.org/10.1049/elp2.12394.

8. Ameid T., Ammar A., Talhaoui H., Azzoug Yo. An automatic rotor bar fault diagnosis using fuzzy logic and DWTenergy for backstepping control driven induction motor in low-speed operation. Soft Computing. 2023;27(15):1-16. https://doi.org/10.1007/s00500-023-08443-y.

9. Kurilin S.P. Induction motors technical condition in-service testing methods. Bulletin of Moscow Power Engineering Institute. 2022;6:11-20. (In Russ.). https://doi.org/10.24160/1993-6982-2022-6-11-20.

10. Khalyasmaa A.I., Revenkov I.S., Sidorova A.V. Application of digital twin technology for analysis and prediction state of power transformer equipment. Kazan State Power Engineering University Bulletin. 2022:14(3):99-113. (In Russ.). EDN: WEXWZR.

11. Bannov D.M., Bannov D.M., Krickij M.V. Development of an experimental setup for studying diagnostic signs of damage to a short-circuited rotor winding of an induction motor. In: Elektroenergetika glazami molodezhi – 2018: materialy IX Mezhdunarodnoj molodezhnoj nauchno-tekhnicheskoj konferencii = Electric Power Industry through the Eyes of Youth – 2018: Proceedings of the 9th International youth scientific and technical conference. 1–5 October 2018, Kazan’. Kazan’: Kazan State Power Engineering University; 2018, vol. 3, р. 237-238. (In Russ.). EDN: ZAXFDV.

12. Shakhnovich D.I. Development of a software and hardware environment for testing relay protection devices based on the microprocessor relay protection and automation terminal manufactured by the Mechatronic Research Centre. In: Problemy geologii i osvoeniya nedr: trudy XXVIII Mezhdunarodnogo molodezhnogo nauchnogo simpoziuma imeni akademika M.A. Usova, posvyashchennogo 125-letiyu so dnya rozhdeniya akademika Akademii nauk SSSR, professora K.I. Satpaeva i 130-letiyu so dnya rozhdeniya chlena-korrespondenta Akademii nauk SSSR, professora F.N. Shahova = Problems of Geology and Subsoil Development: Proceedings of the 28th International Youth Scientific Symposium named after the Academician M.A. Usov dedicated to the 125th birth anniversary of the Academician of the USSR Academy of Sciences, Professor K.I. Satpaev and the 130th birth anniversary of the Corresponding Member of the USSR Academy of Sciences, Professor F.N. Shakhov. 1–5 April 2024, Tomsk. Tomsk: Tomsk Polytechnic University; 2024, р. 231-233. (In Russ.). EDN: BJDKXQ.

13. Draper G., Smith D. Applied regression analysis. New York: John Wiley & Sons; 1981. (Russ. ed.: Prikladnoj regressionnyj analiz. Moscow: Dialektika; 2017, 912 р.)

14. Martyugin S.A., Porshnev S.V. On the possibility of increasing the noise immunity of OFDM telecommunication systems using fractional Fourier transforms. International Journal of Open Information Technologies. 2024;12(7):71- 79. (In Russ.). EDN: UXUJDT.

15. Grishaev D.A., Radzyuk A.Yu., Istyagina E.B. Processing of experimental results for super-cavitating flow past cone by local polynomial regression (LOESS). iPolytech Journal. 2023;27(3):518-526. (In Russ.). https://doi.org/10.21285/1814-3520-2023-3-518-526. EDN: WOSBCK.

16. Pramesti W., Damayanti I., Asfani D. Stator fault identification analysis in induction motor using multinomial logistic regression. In: International Seminar on Intelligent Technology and Its Applications. 2016;439-442. https://doi.org/10.1109/ISITIA.2016.7828700.

17. Chao Zhao. Trend analysis of rotor-to-stator impact-rub based on smooth support vector regression. International Journal of Computer Applications in Technology. 2014;50(3-4):243. https://doi.org/10.1504/IJCAT.2014.066735.

18. Toponen N.A., Gevorkyan M.N. Implementation of three-dimensional projective geometric algebra in a high-level programming language. In: Informacionno-telekommunikacionnye tekhnologii i matematicheskoe modelirovanie vysokotekhnologichnyh sistem: materialy Vserossijskoj konferencii s mezhdunarodnym uchastiem = Information and telecommunication technologies and mathematical modeling of high-tech systems: Proceedings of All-Russian Conference with international participation. 17–21 April 2023, Moscow. M.: Peoples’ Friendship University of Russia; 2023, р. 240-245. (In Russ.). EDN: XHDQSK.

19. Bannov D.M., Polishchuk V.I. Method of processing signals of stator currents of induction motor to diagnose broken rotor bar. Vestnik of the Ivanovo State Power Engineering University. 2024;4:64-72. (In Russ.). https://doi.org/10.17588/2072-2672.2024.4.064-072. EDN: BTWJEE.

20. D’yakonov V.P. MATLAB 6/6.1/6.5 + Simulink 4/5 in mathematics and modeling. Moscow: SOLON-Press; 2008, 576 р. (In Russ.).