Implementation of Digital Twins for electrical energy conversion systems in selected case studies; pp. 19–39

Full article in PDF format | 10.3176/proc.2021.1.03

Anton Rassõlkin, Tamás Orosz, Galina Lvovna Demidova, Vladimir Kuts, Viktor Rjabtšikov, Toomas Vaimann, Ants Kallaste


Reference implementation of Digital Twins for electrical energy conversion systems is an important and open question in the industrial domain. Digital Twins can predict the future performance, behaviour, and maintenance needs of a complex system. Today the concept of Digital Twins is not only an emulation or simulation of the physical object along with its development history but also contains much information from the respective manufacturers and services. This paper presents the current state-of-the-art of Digital Twins in relation to some interesting novel applications from different fields of electrical engineering. The objective of the paper is to give an overview of the successful application of Digital Twins in electrical energy conversion systems, such as industrial robotics and wind turbines; to discuss trends in applications like electric vehicles; and to suggest new applications, such as telescopes. Special attention is paid to the possible application of Digital Twins in faults diagnostics and prognostics of electrical energy conversion systems. Successful implementation of Digital Twins in any electrical energy conversion system diagnostics and prognostics allows for low-cost maintenance, higher utilization of the individual devices and systems, as well as lower usage of material and human resources. A SWOT analysis was performed for Digital Twin applications in electrical energy conversion systems. The latter is a useful analysis technique that explores possibilities for new achievements or solutions to existing problems and makes decisions about the best path.


1. Weyer, S., Schmitt, M., Ohmer, M., and Gorecky, D. Towards industry 4.0-standardization as the crucial challenge for highlymodular, multi-vendor production systems. IFAC-PapersOnLine, 2015, 48(3), 579–584.

2. Li, Q., Jiang, H., Tang, Q., Chen, Y., Li, J., and Zhou, J. Smart manufacturing standardization: reference model and standards framework. In OTM Confederated International Conferences ”On the Move to Meaningful Internet Systems”, October 24–28, 2016, Rhodes, Greece. Lecture Notes in Computer Science, Vol. 10034, Springer, Cham, 2016, 16–25.

3. DIN: A collection of standards concerning Industry 4.0. 

4. Ghobakhloo, M. The future of manufacturing industry: a strategic roadmap toward industry 4.0. J. Manuf. Technol. Manag., 2018, 29(6), 910–936.

5. Stock, T. and Seliger, G. Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 2016, 40, 536–541.

6. El Saddik, A. Digital twins: the convergence of multimedia technologies. IEEE MultiMedia, 2018, 25(2), 87–92.

7. Sharma, M. and George, J. Digital twin in the automotive industry: driving physical-digital convergence. 2018. 

8. Tao, F. and Zhang, M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access, 2017, 5, 20418–20427.

9. Grieves, M. and Vickers, J. Digital twin: mitigating unpre­dictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (Kahlen, F. J., et al., eds). Springer, Cham, 2017, 85–113.

10. Barricelli, B. R., Casiraghi, E., and Fogli, D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access, 2019, 7, 167653–167671.

11. Rosen, R., von Wichert, G., Lo, G., and Bettenhausen, K. D. About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 2015, 48(3), 567–572.

12. Weyer, S., Meyer, T., Ohmer, M., Gorecky, D., and Zuhlke, D. Future modeling and simulation of CPS-based factories: an example from the automotive industry. IFAC-PapersOnLine, 2016, 49(31), 97–102.

13. Orosz, T. Evolution and modern approaches of the power transformer cost optimization methods. Periodica Poly­technica Electrical Engineering and Computer Science, 2019, 63(1), 37–50.

14. Abetti, P., Cuthbertson, W., and Williams, S. Philosophy of applying digital computers to the design of electric apparatus. In Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, 1958, 77(3), 367–379.

15. Boschert, S. and Rosen, R. Digital twin–the simulation aspect. In Mechatronic Futures (Hehenberger, P., Bradley, D., eds). Springer, Cham, 2016, 59–74.

16. ANSYS: Engineering simulation & 3D design software. 

17. Zhang, L., Wang, W., and Shi, Y. Research on maximum power point tracking based on an improved fuzzy-PD dual-mode algorithm. In Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), October 14–16, 2017, Shanghai, China. IEEE, 2017, 1–6.

18. Alexandrov, N. M., Hussaini, M. Y. (eds). Multidisciplinary Design Optimization: State of the Art. SIAM, Philadelphia, PA, 1997.

19. Karban, P., Mach, F., Kŭs, P., Pánek, D., and Doležel, I. Numerical solution of coupled problems using code Agros2D. Computing, 2013, 95(1), 381–408.

20. Karban, P., Pánek, D., Orosz, T., Petrášová, I., and Doležel, I. FEM based robust design optimization with Agros and Ārtap. Comput. Math. Appl., 2020. https://doi.org/10.1016/ j.camwa.2020.02.010

21. Tóth, B. Multi-field dual-mixed variational principles using non-symmetric stress field in linear elastodynamics. J. Elast., 2016, 122(1), 113–130.

22. Haag, S. and Anderl, R. Digital twin – proof of concept. Manuf. Lett., 2018, 15, 64–66.

23. Madni, A. M., Madni, C. C., and Lucero, S. D. Leveraging digital twin technology in model-based systems engineering. Systems, 2019, 7(1), 7.

24. Xu, G. and Xia, L. Short-term prediction of wind power based on adaptive LSTM. In Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), October 20–22, 2018, Beijing, China, 1–5.

25. Zhang, L. Specification and design of cyber physical systems based on system of systems engineering approach. In Proceedings of the 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)October 19–23, 2018, Wuxi, China, 300–303.

26. Peter, G. P. Calculations for short circuit withstand capability of a distribution transformer. Annals of Faculty Engineering Hunedoara. International Journal of Engineering, 2011, 9(3), 243–246.

27. Barrère, M., Hankin, C., Barboni, A., Zizzo, G., Boem, F., Maffeis, S., et al. CPS-MT: a real-time cyber-physical system monitoring tool for security research. In Proceedings of the 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), August 28–31, 2018,Hakodate, Japan, 240–241.

28. Schutzer, K., de Andrade Bertazzi, J., Sallati, C., Anderl, R., and Zancul, E. Contribution to the development of a digital twin based on product lifecycle to support the manufacturing process. Procedia CIRP, 2019, 84, 82–87.

29. Schluse, M. and Rossmann, J. From simulation to experimentable digital twins: simulation-based development and operation of complex technical systems. In Proceedings of the 2016 IEEE International Symposium on Systems Engineering (ISSE), October 3–5, 2016, Edinburgh, UK, 1–6.

30. Iskhakova, A., Iskhakov, A., Meshcheryakov, R., and Jharko, E. Method of verification of robotic group agents in the conditions of communication facility suppression. IFAC-PapersOnLine, 2019, 52(13), 1397–1402.

31. He, B., Wang, S., and Liu, Y. Underactuated robotics: a review. Int. J. Adv. Robot. Syst., 2019, 16(4), 1729881419 862164.

32. Li, X., Luo, X., Wang, J., Zhu, Y., and Guan, X. Bearing-based formation control of networked robotic systems with parametric uncertainties. Neurocomputing, 2018, 306, 234–245.

33. Khalaji, A. K. Modeling and control of uncertain multibody wheeled robots. Multibody Syst. Dyn., 2019, 46(3), 257– 279.

34. International Federation of Robotics. 

35. Kangru, T., Riives, J., Otto, T., Pohlak, M., and Mahmood, K. Intelligent decision making approach for performance evaluation of a robot-based manufacturing cell. In Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition, November 9–15, 2018, Pittsburgh, PA, USA. 

36. Kuts, V., Sarkans, M., Otto, T., Tähemaa, T., and Bondarenko, Y. Digital twin: concept of hybrid pro­gramming for industrial robots–use case. In Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition, November 11–14, 2019, Salt Lake City, UT, USA. 

37. Kuts, V., Otto, T., Tähemaa, T., Bukhari, K., and Pataraia, T. Adaptive industrial robots using machine vision. In Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition, November 9–15, 2018, Pittsburgh, PA, USA. 
https://doi.org/10.1115/IMECE 2018-86720

38. Sell, R., Coatanea, E., and Christophe, F. Important aspects of early design in mechatronic. In Proceedings of the 6th International Conference of DAAAM Baltic Industrial Engineering, April 24–26, 2008, Tallinn, Estonia, 177182.

39. Statista – The Statistics Portal for Market Data, Market Research and Market Studies. 

40. Estonian Road Administration, Traffic Safety Programme 20162025. 

41. Electric mobility in Germany. 

42. Autostat. 

43. Rassõlkin, A. and Vodovozov, V. A test bench to study propulsion drives of electric vehicles. In Proceedings of the International Conference-Workshop Compatibility in Power Electronics (CPE), June 5–7, 2013, Ljubljana, Slovenia. IEEE, 2013, 275–279.

44. Rassõlkin, A., Vaimann, T., Kallaste, A., and Kuts, V. Digital twin for propulsion drive of autonomous electric vehicle. In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), October 7–9, 2019, Riga, Latvia, 1–4.

45. Mi, C. and Masrur, M. A. Hybrid Electric Vehicles: Principles and Applications with Practical Perspectives, 2nd ed. John Wiley & Sons, 2017.

46. Martínez, C. M., Hu, X., Cao, D., Velenis, E., Gao, B., and Wellers, M. Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective. IEEE Transactions on Vehicular Technology, 2016, 66(6), 4534–4549.

47. Senanayaka, J. S. L., Khang, H. V., and Robbersmyr, K. G. Multiple classifiers and data fusion for robust diagnosis of gearbox mixed faults. IEEE Transactions on Industrial Informatics, 2018, 15(8), 4569–4579.

48. Rassõlkin, A., Kallaste, A., and Vaimann, T. Dynamic control system for electric motor drive testing on the test bench. In Proceedings of the 2015 9th International Conference on Compatibility and Power Electronics (CPE), June 24–26, 2015, Costa da Caparica, Portugal. IEEE, 252–257.

49. Kaban, S., Dong, Z., and Crawford, C. Performance modeling and benchmark analysis of an advanced 4WD series-parallel PHEV using dynamic programming. In Proceedings of the 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), October 19–22, 2015, Montreal, QC, Canada, 698–704.

50. Rassõlkin, A. and Vodovozov, V. Experimental setup to explore the drives of battery electric vehicles. World Electr. Veh. J., 2013, 6(4), 1109–1114.

51. Sell, R., Aryassov, G., Petritshenko, A., and Kaeeli, M. Kinematics and dynamics of configurable wheel-leg. In Proceedings of the 8th International Conference of DAAAM Baltic Industrial Engineering, April 19–21, 2012, Tallinn, Estonia, 345–351.

52. Rassõlkin, A., Sell, R., and Leier, M. Development case study of the first Estonian self-driving car, ISEAUTO. Electrical, Control and Communication Engineering, 2018, 14(1), 81–88.

53. Kulik, E., Tran, X. T., and Anuchin, A. Estimation of the requirements for hybrid electric powertrain based on analysis of vehicle trajectory using GPS and accelerometer data. In Proceedings of the 2018 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED)January 31–February 2, 2018, Moscow, Russia. IEEE, 2018, 1–5.

54. Alam, K. M. and El Saddik, A. C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE access, 2017, 5, 2050–2062.

55. Sell, R., Leier, M., Rassõlkin, A., and Ernits, J.-P. Self-driving car ISEAUTO for research and education. In Proceedings of the 2018 19th International Conference on Research and Education in Mechatronics (REM), June 7–8, 2018, Delft, the Netherlands. IEEE, 2018, 111–116.

56. Daily, M., Medasani, S., Behringer, R., and Trivedi, M. Self-driving cars. Computer, 2017, 50(12), 18–23.

57. Sell, R., Leier, M., Rassõlkin, A., and Ernits, J.-P. Autonomous last mile shuttle ISEAUTO for education and research. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 2020, 10(1), 18–30.

58. Kalra, N. and Paddock, S. M. Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp. Res. Part A Policy Pract., 2016, 94, 182–193.

59. Xu, Y., Zou, Y., and Sun, J. Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications. J. Intell. Connect. Veh., 2018, 1(1), 1–4.

60. ISEAUTO Project. 

61. Glaessgen, E. H. and Stargel, D. S. The digital twin paradigm for future NASA and US Air Force vehicles. In Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference: Special session on the Digital Twin-, April 23–26, 2012, Honolulu, HI, USA. 

62. Shubenkova, K., Valiev, A., Mukhametdinov, E., Shepelev, V., Tsiulin, S., and Reinau, K. H. Possibility of digital twins technology for improving efficiency of the branded service system. In Proceedings of the 2018 Global Smart Industry Conference (GloSIC), November 13–15, 2018, Chelyabinsk, Russia. IEEE, New York, NY, 2018, 1–7.

63. Brunner, P., Denk, F., Huber, W., and Kates, R. Virtual safety performance assessment for automated driving in complex urban traffic scenarios. In Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), October 27–30, 2019, Auckland, New Zealand, 679–685.

64. Sesto, E. and Lipman, N. H. Wind energy in Europe. Wind Engineering, 1992, 16(1), 3547.

65. Wind energy in Europe in 2019. 

66. Electricity generation – Energy Charts. 

67. Tarbimine ja tootmine (production and consumption) – Elering LIVE. 

68. Orlova, S., Rassõlkin, A., Kallaste, A., Vaimann, T., and Belahcen, A. Lifecycle analysis of different motors from the standpoint of environmental impact. Latvian Journal of Physics and Technical Sciences, 2016, 53(6), 37–46.

69. Sivalingam, K., Sepulveda, M., Spring, M., and Davies, P. A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective. In Proceedings of the 2018 2nd International Conference on Green Energy and Applications (ICGEA), March 24–26, 2018, Singapore. IEEE, 2018, 197–204.

70. Oñederra, O., Asensio, F. J., Eguia, P., Perea, E., Pujana, A., and Martinez, L. MV cable modeling for application in the digital twin of a windfarm. In Proceedings of the 2019 International Conference on Clean Electrical Power (ICCEP), July 2–4, 2019, Otranto, Italy. IEEE, New York, NY, 617–622.

71. Ebrahimi, A. Challenges of developing a digital twin model of renewable energy generators. In Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), June 12–14, 2019, Vancouver, BC, Canada, 1059–1066.

72. Weigelt, M., Kink, J., Mayr, A., v. Lindenfels, J., Kuhl, A., and Franke, J. Digital twin of the linear winding process based on explicit finite element method. 2019 9th International Electric Drives Production Conference (EDPC), February 27, 2019, Esslingen, Germany, 1–7.

73. Fact sheet GE power & water renewable energy 
https://www.ge.org (accessed 2020-03-16).

74. Digital twin software – enhance asset and process performance. 

75. Digital manufacturing efficiency – PTC. 

76. Vaimann, T., Kudrjavtsev, O., Kilk, A., Kallaste, A., and Rassõlkin, A. Design and prototyping of directly driven outer rotor permanent magnet generator for small scale wind turbines. Adv. Electr. Electron. Eng., 2018, 16(3), 271–278.

77. Lukin, A., Demidova, G. L., Lukichev, D. V., Rassõlkin, A., Kallaste, A., Vaimann, T., et al. Experimental prototype of high-efficiency wind turbine based on magnus effect. In Proceedings of the 2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversary (IWED), January 27–30, 2020, Moscow, Russia. IEEE, 2020, 1–6.

78. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., and Sui, F. Digital twin-driven product design, manufacturing and service with big data.  Int. J. Adv. Manuf. Technol., 2018, 94, 3563–3576.

79. Tao, F., Zhang, M., Liu, Y., and Nee, A. Y. C. Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 2018, 67(1), 169–172.

80. Pargmann, H., Euhausen, D., and Faber, R. Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: a quantitative approach. In Proceedings of the 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), April 20–22, 2018, Chengolu, China, 233–237.

81. Imaie, E., Sheikholeslami, A., and Ahmadi Ahangar, R. Improving short-term wind power prediction with neural network and  ICA algorithm and input feature selection. Journal of Advances in Computer Research, 2014, 5(3), 13–34.

82. AhmadiAhangar, R., Rosin, A., Niaki, A. N., Palu, I., and Korõtko, T. A review on real-time simulation and analysis methods of microgrids. International Transactions on Electrical Energy Systems, 2019, 29(11), e12106.

83. Demidova, G. L., Lukichev, D. V., and Kuzin, A. Y. A genetic approach for auto-tuning of adaptive fuzzy PID control of a telescope’s tracking system. Procedia Computer Science, 2019, 150, 495–502.

84. Chen, Z., Zhang, R., Chen, Z., Yang, S., and Hu, Q.-Q. Experiment and modal analysis on the primary mirror structure of space solar telescope. In Proceedings of the SPIE. Space Telescopes and Instrumentation I: Optical, Infrared, and Millimeter, May 24–31, 2006, Orlando, FL, USA, 62654B.

85. Kracht, K., v. Wagner, U., and Segert, T. Analysis of the vibration behavior of the Dobson space telescope. In Proceedings in Applied Mathematics and Mechanics (PAMM), 2007, 7(1), 4050035–4050036.

86. Bely, P. Y. (ed.). The Design and Construction of Large Optical Telescopes. Springer, New York, NY, 2003.

87. Withington, S. and Murphy, J. A. Modal analysis of partially coherent submillimeter-wave quasi-optical systems. IEEE Trans. Antennas Propag., 1998, 46(11), 1651–1659.

88. Aubrun, J.-N., Lorell, K. R., Havas, T. W., and Henninger, W. C. Performance analysis of the segment alignment control system for the ten meter telescope. Automatica, 1988, 24(4), 437–453.

89. Schipani, P. and Mancini, D. Modeling the VST telescope and the effect of the wind disturbance on its performance. IFAC Proceedings Volumes, 2002, 35(1), 179–185.

90. Lukichev, D. V., Demidova, G. L., and Brock, S. Comparison of adaptive fuzzy PID and ANFIS controllers for precision positioning of complex object with nonlinear disturbance – study and experiment. In Proceedings of the 2018 20th European Conference on Power Electronics and Applications (EPE’18 ECCE Europe), September 17–21, 2018, Riga, Latvia. IEEE, New York, NY, P.1–P.9.

91. Molfese, C., Schipani, P., Capaccioli, M., Sedmak, G., and D’Orsi, S. Survey telescope control electronics. In Proceedings of the 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, June 11–13, 2008, Ischia, Italy. IEEE, 2008, 523–527.

92. Costa, A., Sciacca, E., Vitello, F., Becciani, U., Massimino, P., Riggi, S., et al. An integrated workspace for the Cherenkov Telescope Array. Future Gener. Comput. Syst., 2019, 94, 811–819.

93. Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine, 2018, 51(11), 1016–1022.

94. Hehenberger, P. and Bradley, D. Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and their Designers. Springer, Cham, 2016.

95. Orosz, T., Sörés, P., Raisz, D., and Tamus, Z. Á. Analysis of the green power transition on optimal power transformer designs. Period. Polytech. Electr. Eng. Comput. Sci., 2015, 59(3), 125–131.

96. Dean, J. Pricing Policies for New Products. HBR Classics, 1976.

97. Wolpert, D. H. and Macready, W. G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput., 1997, 1(1), 67–82.

98. Pánek, D., Orosz, T., and Karban, P. Artap: Robust design optimization framework for engineering applications. arXiv preprint arXiv:1912.11550, 2019.

99. Burnett, D., Thorp, J., Richards, D., Gorkovenko, K., and Murray-Rust, D. Digital twins as a resource for design research. In Proceedings of the 8th ACM International Symposium on Pervasive Displays, June 12–14, 2019, Palermo, Italy. ACM, New York, NY, 1–2.

100. Cerrone, A., Hochhalter, J. D., Heber, G., and Ingraffea, A. R. On the effects of modeling as-manufactured geometry: toward digital twin. Int. J. Aerosp. Eng.2014(3), 439278.

101. Kuts, V., Modoni, G. E., Otto, T., Sacco, M., Tähemaa, T., Bondarenko, Y., et al.  Synchronizing physical factory and its digital twin through an IioT middleware: a case study. Proc. Estonian Acad. Sci., 2019, 68(4), 364–370.

102. Vaimann, T., Rassõlkin, A., Kallaste, A., Pomarnacki, R., Belahcen, A., and Hyunh, V. K. Artificial intelligence in monitoring and diagnostics of electrical energy conversion systems. Proceeding of 27th International Workshop on Electric Drives (IWED2020), January 27–30, 2020, Moscow, Russia. IEEE, 2020,9069566.

103. Zhang, M., Zuo, Y., and Tao, F. Equipment energy consumption management in digital twin shop-floor: a framework and potential applications. In Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), March 27–29, 2018, Zhuhai, China, 1–5.

104. Karanjkar, N., Joglekar, A., Mohanty, S., Prabhu, V., Raghunath, D., and Sundaresan, R. Digital twin for energy optimization in an SMT-PCB assembly line. In Proceedings of the 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), November 1–3, 2018, Bali, Indonesia, 85–89.

105. Poór, P., Kuchtová, N., and Šimon, M. Machinery maintenance as part of facility management. Procedia Eng., 2014, 69, 1276–1280.

106. Kandukuri, S. T., Senanyaka, J. S. L., Hyunh, V. K., Robbersmyr, K. G., et al. A two-stage fault detection and classification scheme for electrical pitch drives in offshore wind farms using support vector machine. IEEE Trans. Ind. Appl., 2019, 55(5), 5109–5118.

107. Orłowska-Kowalska, T., Kowalski, C. T., and Dybkowski, M. Fault-diagnosis and fault-tolerant-control in industrial processesand electrical drives. In Advanced Control of Electrical Drives and Power Electronic Converters. Studies in Systems, Decision and Control, Vol. 75. Springer, Cham, 2017, 101–120.

108. Kabziński, J. (ed.). Advanced Control of Electrical Drives and Power Electronic Converters. Springer, 2017.

109. Vaimann, T. Diagnostics of induction machine rotor faults using analysis of stator signals. PhD thesis, Department of Electrical Engineering, Tallinn University of Technology, 2014.

110. Furtat, I. B. An algorithm to control nonlinear systems in perturbations and measurement noise. Autom. Remote Control, 2018, 79, 1207–1221.

111. Margun, A., Furtat, I., Bazylev, D., and Kremlev, A. Disturbance compensation and control algorithm with application for non-linear twin rotor MIMO system. In Mechatronics 2027. Advances in Intelligent Systems and Computing, Vol. 644. Springer, Cham, 2017, 428–435.

112. Furtat, I. B. and Fradkov, A. L. Robust control of multi-machine power systems with compensation of disturbances. Int. J. Electr. Power Energy Syst., 2015, 73, 584–590.

113. Vas, P. Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Oxford University Press, 1993.

114. Thorsen, O. V. and Dalva, M. A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries. IEEE Trans. Ind. Appl., 1995, 31(5), 1186–1196.

115. Petrov, A., Plokhov, I., Rassõlkin, A., Vaimann, T., Kallaste, A., and Belahcen, A. Adjusted electrical equivalent circuit model of induction motor with broken rotor bars and eccentricity faults. In Proceeding of the 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), August 29–September 1, 2017, Tinos, Greece, 58–64.

116. Asad, B., Vaimann, T., Belahcen, A., Kallaste, A., Rassõlkin, A., and Iqbal, M. N. Broken rotor bar fault detection of the grid and inverter-fed induction motor by effective attenuation of the fundamental component, 2019. 

117. Asad, B., Vaimann, T., Kallaste, A., Rassõlkin, A., Belahcen, A., and Iqbal, M. N. Improving legibility of motor current spectrum for broken rotor bars fault diagnostics. Electrical, Control and Communication Engineering, 2019, 15(1), 1–8.

118. Pando-Acedo, J., Rassõlkin, A., Lehikoinen, A., Vaimann, T., Kallaste, A., Romero-Cadaval, E., and Belahcen, A. Hybrid FEA-Simulink modelling of permanent magnet assisted synchronous reluctance motor with unbalanced magnet flux. In Proceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)August 27–30, 2019, Toulouse, France, 174–180.

119. Kudelina, K., Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A., and Lukichev, D. V. Main faults and dia­gnostic possibilities of BLDC motors. In Proceedings of the 2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversary (IWED), January 27–30, 2020,Moscow, Russia. IEEE, 2020, 1–6.

120. Lee, K.-B. and Choi, U.-M. Faults and diagnosis systems in power converters. Advanced and Intelligent Control in Power Electronics and Drives. Studies in Computational Intelligence, Vol. 531. Springer, Cham, 2014, 143–178.

121. Yang, S., Xiang, D., Bryant, A., Mawby, P., Ran, L., and Tavner, P. Condition monitoring for device reliability in power electronic converters: a review. IEEE Trans. Power Electron., 2010, 25(11), 2734–2752.

122. Ruiming, F., Minling, W., Xinhua, G., Rongyan, S., Pengfei, S., et al. Identifying early defects of wind turbine based on SCADA data and dynamical network marker. Renew. Energ., 2020, 154, 625–635.

123. Kim, H.-C., Kim, M.-H., and Choe, D.-E. Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals. Ocean Eng., 2019, 188, 106226.

124. Tao, F., Zhang, M., and Nee, A. Y. C. Digital Twin Driven Smart Manufacturing. Academic Press, 2019.

125. Lermer, M. and Reich, C. Creation of digital twins by combining fuzzy rules with artificial neural networks. In Proceedings of the IECON 2019 – 45th Annual Conference of the IEEE Industrial Electronics Society, October 14–17, 2019, Lisbon, Portugal, 5849–5854.

126. Kuts, V., Modoni, G. E., Terkaj, W., Tähemaa, T., Sacco, M., and Otto, T. Exploiting factory telemetry to support virtual reality simulation in robotics cell. In Proceedings of the International Conference on Augmented Reality, Virtual Reality and Computer Graphics, June 12–15, 2017, Ugento, Italy. Springer, Cham, 2017, 212–221.

127. Kuts, V., Sarkans, M., Otto, T., and Tähemaa, T. Collaborative work between human and industrial robot in manufacturing by advanced safety monitoring system. In Proceedings of the 28th DAAAM International Symposium on Intelligent Manufacturing and Automation, November 8–11, 2017, Zadar, Croatia. DAAAM International, Vienna, 2017, 0996–1001.

128. Shevtshenko, E., Karaulova, T., Igavens, M., Strods, G., Tandzegolskiene, I., Tutlys, V., et al. Dissemination of engineering education at schools and its adjustment to needs of enterprises. In Proceedings of the 28th DAAAM International Symposium on Intelligent Manufacturing and Automation, November 8–11, 2017, Zadar, Croatia. DAAAM International, Vienna, 2017, 44–53.

129. Sell, R. Remote laboratory portal for robotic and embedded system experiments. International Journal of Online and Biomedical Engineering (iJOE), 2013, 9(S8), 23–26.

130. Mark, C. P. and Kamath, S. Review of active space debris removal methods. Space Policy, 2019, 47, 194–206.

131. Schildknecht, T., Hugentobler, U., and Verdun, A. Optical observations of space debris with the Zimmerwald 1-meter telescope. Adv. Space Res., 1997, 19(2), 221–228.

132. ITMO.NEWS. Watching the skies: Roscosmos installs a new set-up for monitoring space debris. 

133. ROSCOSMOS. State Space Corporation. 

134. Arditti, D. Setting-up a small observatory: from concept to construction. Springer Science & Business Media, New York, NY, 2007.

135. Gomez, E. L. and Fitzgerald, M. T. Robotic telescopes in education. Astronomical Review, 2017, 13(1), 28–68.

136. Bresina, J., Drummond, M., Swanson, K., and Edgington, W. Automated management and scheduling of remote automatic telescopes. Optical Astronomy from the Earth and Moon. ASP Conference Series, 1994, 55, 216–233.

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