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Articles

Vol. 8 (2021)

An Overview of Digital Twin Concept for Key Components of Renewable Energy Systems

DOI
https://doi.org/10.31875/2409-9694.2021.08.4
Submitted
December 20, 2021
Published
2021-12-20

Abstract

Renewable energy (RE) is green and low-carbon energy, which can not only protect the environment, promote the technological diversification of the energy supply system, accelerate the adjustment of energy structure, but also has important significance for the sustainable development of economy. With the increasing complexity of the problems of renewable energy system asset management and ensuring the operational reliability of electric power equipment, it's necessary to establish remote, online, reliable monitoring and inspection techniques for the state evaluation of electrical equipment during the full life cycle. In order to meet these demands, the digital twin is a very suitable technology. In recent years, there are numerous scientific papers demonstrating DT's capabilities in virtual simulation, condition monitoring (CM), power optimization and fault diagnosis for RE generation systems, transmission and transformation equipment and storage systems. The majority of the research focusing on product design, maintenance of operation, condition monitoring and fault decision-making has provided many valuable contributions to academia and industrial fields. Nevertheless, all this valuable information is scattered over many literatures and it is lack of systematic generalization. In this article, different applications of DT technology in RE system are analyzed, advanced methods and theories are summarized comprehensively, and the development trend of DT technology in renewable energy system in the future is introduced.

References

  1. Y. He et al., "An overview of acoustic emission inspection and monitoring technology in the key components of renewable energy systems," Mechanical Systems and Signal Processing, vol. 148, p. 107146, 2021. https://doi.org/10.1016/j.ymssp.2020.107146
  2. J. Carroll, A. McDonald, and D. McMillan, "Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines," Wind Energy, vol. 19, no. 6, pp. 1107-1119, 2016. https://doi.org/10.1002/we.1887
  3. W. Vachon, "Long-term O&M costs of wind turbines based on failure rates and repair costs," in Proceedings WINDPOWER, American Wind Energy Association annual conference, Portland, OR, 2002, pp. 2-5.
  4. K. Sivalingam, M. Sepulveda, M. Spring, and P. Davies, "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 2018 2nd international conference on green energy and applications (ICGEA), 2018: IEEE, pp. 197-204. https://doi.org/10.1109/ICGEA.2018.8356292
  5. S. Zhang, S. Wang, and L. Zhao, "The Life Cycle State Evaluation of Electrical Equipment based on Digital Twins," in 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), 2020: IEEE, pp. 1-4. https://doi.org/10.1109/ICHVE49031.2020.9279568
  6. X. Qu, Y. Song, D. Liu, X. Cui, and Y. Peng, "Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model," Microelectronics Reliability, vol. 114, 2020. https://doi.org/10.1016/j.microrel.2020.113857
  7. Y. Wang, G. Zhang, R. Chen, Z. Liu, and R. Qiu, "Analysis of digital twin application of urban rail power supply system for energy saving," presented at the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), 2021. https://doi.org/10.1109/DTPI52967.2021.9540127
  8. M. Liu, S. Fang, H. Dong, and C. Xu, "Review of digital twin about concepts, technologies, and industrial applications," Journal of Manufacturing Systems, vol. 58, pp. 346-361, 2021. https://doi.org/10.1016/j.jmsy.2020.06.017
  9. A. Khalyasmaa, S. Eroshenko, D. Shatunova, A. Larionova, and A. Egorov, "Digital twin technology as an instrument for increasing electrical equipment reliability," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 836, no. 1: IOP Publishing, p. 012005. https://doi.org/10.1088/1757-899X/836/1/012005
  10. S. Bhattacharjee and C. Nandi, "Implementation of industrial internet of things in the renewable energy sector," in The Internet of Things in the Industrial Sector: Springer, 2019, pp. 223-259. https://doi.org/10.1007/978-3-030-24892-5_10
  11. Y. Zheng, S. Yang, and H. Cheng, "An application framework of digital twin and its case study," Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 3, pp. 1141-1153, 2018. https://doi.org/10.1007/s12652-018-0911-3
  12. F. Tao, Y. Cheng, J. Cheng, M. Zhang, W. Xu, and Q. Qi, "Theories and technologies for cyber-physical fusion in digital twin shop-floor," 2017.
  13. F. Tao, Y. Cheng, L. Zhang, and AY. Nee, "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, vol. 28, no. 5, pp. 1079-1094, 2017. https://doi.org/10.1007/s10845-015-1042-8
  14. F. Tao, M. Zhang, J. Cheng, and Q. Qi, "Digital twin workshop: a new paradigm for future workshop," Computer Integrated Manufacturing Systems, vol. 23, no. 1, pp. 1-9, 2017.
  15. J. Tang, S. Soua, C. Mares, and TH. Gan, "An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades," Renewable Energy, vol. 99, pp. 170-179, 2016. https://doi.org/10.1016/j.renene.2016.06.048
  16. W. Yang and S. W. Tian, "Research on a power quality monitoring technique for individual wind turbines," Renewable Energy, vol. 75, pp. 187-198, 2015. https://doi.org/10.1016/j.renene.2014.09.037
  17. E. Branlard, D. Giardina, and CS. Brown, "Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations," Wind Energy Science, vol. 5, no. 3, pp. 1155-1167, 2020. https://doi.org/10.5194/wes-5-1155-2020
  18. F. Pimenta, J. Pacheco, C. Branco, C. Teixeira, and F. Magalhães, "Development of a digital twin of an onshore wind turbine using monitoring data," in Journal of Physics: Conference Series, 2020, vol. 1618, no. 2: IOP Publishing, p. 022065. https://doi.org/10.1088/1742-6596/1618/2/022065
  19. S. S. Johansen, "On developing a digital twin for fault detection in drivetrains of offshore wind turbines," NTNU, 2018.
  20. S. Wakayama and A. Mizutani, "AE Analysis of Damage Process in Thin Film Solar Cells under Mechanical Strain " presented at the 29th European Conference on Acoustic Emission Testing, Wien 2010.
  21. H. Tazawa, T. Sakai, and S. Wakayama, "Characterization of Damage in a-Si/a-SiGe Flexible Solar Cells under Mechanical Strain by AE Technique and Lock-in Thermography," presented at the 28th European Photovoltaic Solar Energy Conference and Exhibition, Paris, 2013.
  22. Y. Kishi et al., "Ultralight flexible Amorphous silicon solar cell and its application to an airplane," Solar Energy Materials, vol. 23, no. 2-4, pp. 312-318, 1991. https://doi.org/10.1016/0165-1633(91)90135-8
  23. O. Mori et al., "First Solar Power Sail Demonstration by IKAROS," Transaction on The Japan Society for Aeronautical and Space Sciences, Aerospace Technology, vol. 8, no. 27, pp. To_4_25-To_4_31, 2010. https://doi.org/10.2322/tastj.8.To_4_25
  24. B. Lu and X. Zhou, "Quality and reliability oriented maintenance for multistage manufacturing systems subject to condition monitoring," Journal of Manufacturing Systems, vol. 52, pp. 76-85, 2019. https://doi.org/10.1016/j.jmsy.2019.04.003
  25. MJ. Hossain et al., "A Comprehensive Methodology to Evaluate Losses and Process Variations in Silicon Solar Cell Manufacturing," IEEE Journal of Photovoltaics, vol. 9, no. 5, pp. 1350-1359, 2019. https://doi.org/10.1109/JPHOTOV.2019.2926628
  26. FQ. Pei, YF. Tong, MH. Yuan, K. Ding, and XH. Chen, "The digital twin of the quality monitoring and control in the series solar cell production line," Journal of Manufacturing Systems, vol. 59, pp. 127-137, 2021. https://doi.org/10.1016/j.jmsy.2021.02.001
  27. L. Massel, N. Shchukin, and A. Cybikov, "Digital twin development of a solar power plant," in E3S Web of Conferences, 2021, vol. 289: EDP Sciences. https://doi.org/10.1051/e3sconf/202128903002
  28. SK. Andryushkevich, SP. Kovalyov, and E. Nefedov, "Composition and application of power system digital twins based on ontological modeling," in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, vol. 1: IEEE, pp. 1536-1542. https://doi.org/10.1109/INDIN41052.2019.8972267
  29. R. Asimov, S. Chernoshey, I. Kruse, and V. Osipovich, "Digital twin in the Analysis of a Big Data," Big Data and Advanced Analytics, no. 4, pp. 70-79, 2018.
  30. L. Bai, Y. Zhang, H. Wei, J. Dong, and W. Tian, "Digital Twin Modeling of a Solar Car Based on the Hybrid Model Method with Data-Driven and Mechanistic," Applied Sciences, vol. 11, no. 14, 2021. https://doi.org/10.3390/app11146399
  31. J. Walsh, I. Bashir, P. R. Thies, L. Johanning, and P. Blondel, "Acoustic emission health monitoring of marine renewables: Illustration with a wave energy converter in Falmouth Bay (UK)," in OCEANS 2015-Genova, 2015: IEEE, pp. 1-7. https://doi.org/10.1109/OCEANS-Genova.2015.7271455
  32. J. Walsh, I. Bashir, J. K. Garrett, PR. Thies, P. Blondel, and L. Johanning, "Monitoring the condition of marine renewable energy devices through underwater acoustic emissions: Case study of a wave energy converter in Falmouth Bay, UK," Renewable Energy, vol. 102, pp. 205-213, 2017. https://doi.org/10.1016/j.renene.2016.10.049
  33. G. Reikard, B. Robertson, and JR. Bidlot, "Wave energy worldwide: Simulating wave farms, forecasting, and calculating reserves," International journal of marine energy, vol. 17, pp. 156-185, 2017. https://doi.org/10.1016/j.ijome.2017.01.004
  34. P. Qian, B. Feng, D. Zhang, X. Tian, and Y. Si, "IoT‐based approach to condition monitoring of the wave power generation system," IET Renewable Power Generation, vol. 13, no. 12, pp. 2207-2214, 2019. https://doi.org/10.1049/iet-rpg.2018.5918
  35. A. Cichoń and P. Berger, "Possibility of using acoustic emission method for testing load tap changers during normal operation of the transformer," presented at the 2014 International Conference on High Voltage Engineering and Application, Poznan, 2014. https://doi.org/10.1109/ICHVE.2014.7035479
  36. Y. Yang et al., "State Evaluation of Power Transformer Based on Digital Twin," in 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2019: IEEE, pp. 230-235. https://doi.org/10.1109/SOLI48380.2019.8955043
  37. IE. Kolesnikov, AV. Korzhov, and KE. Gorshkov, "Digital Program for Diagnosing the Status of a Power Transformer," in 2020 Global Smart Industry Conference (GloSIC), 2020: IEEE, pp. 315-321. https://doi.org/10.1109/GloSIC50886.2020.9267867
  38. W. Boeck, "Insulation Co-Ordination of GIS, Return of Experience, On Site Tests and Diagnostic Techniques," Electra, no. 176, pp. 67-97, 1998.
  39. Z. Jiang, Y. Guo, and Z. Wang, "Digital twin to improve the virtual-real integration of industrial IoT," Journal of Industrial Information Integration, vol. 22, 2021. https://doi.org/10.1016/j.jii.2020.100196
  40. AM. Madni, CC. Madni, and SD. Lucero, "Leveraging digital twin technology in model-based systems engineering," Systems, vol. 7, no. 1, p. 7, 2019. https://doi.org/10.3390/systems7010007
  41. B. Pang, B. Zhu, X. Wei, S. Wang, and R. Li, "On-line monitoring method for long distance power cable insulation," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 1, pp. 70-76, 2016. https://doi.org/10.1109/TDEI.2015.004995
  42. Yq. Hao, Yl. Cao, Q. Ye, Hw. Cai, and Rh. Qu, "On-line temperature monitoring in power transmission lines based on Brillouin optical time domain reflectometry," Optik-International Journal for Light and Electron Optics, vol. 126, no. 19, pp. 2180-2183, 2015. https://doi.org/10.1016/j.ijleo.2015.05.111
  43. X. Chen, J. Smit, and S. Meijer, "Investigation on insulation reliability of 380 kV XLPE cable systems," in 2011 Electrical Insulation Conference (EIC). 2011: IEEE, pp. 434-438. https://doi.org/10.1109/EIC.2011.5996193
  44. O. Kähler, S. Hochstöger, G. Kemper, and J. Birchbauer, "Automating powerline inspection: A novel multisensor system for data analysis using deep learning," The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 43, pp. 747-754, 2020. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-747-2020
  45. W. He et al., "Research on the Application of Digital Twin Technique in High Voltage Cable," in 2020 4th International Conference on Power and Energy Engineering (ICPEE), 2020: IEEE, pp. 90-93. https://doi.org/10.1109/ICPEE51316.2020.9311058
  46. B. Dunn, H. Kamath, and JM. Tarascon, "Electrical energy storage for the grid: a battery of choices," Science, vol. 334, no. 6058, pp. 928-935, 2011. https://doi.org/10.1126/science.1212741
  47. B. Wu, WD. Widanage, S. Yang, and X. Liu, "Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems," Energy and AI, vol. 1, p. 100016, 2020. https://doi.org/10.1016/j.egyai.2020.100016
  48. T. Chen et al., "Applications of lithium-ion batteries in grid-scale energy storage systems," Transactions of Tianjin University, vol. 26, no. 3, pp. 208-217, 2020. https://doi.org/10.1007/s12209-020-00236-w
  49. Y. Peng, X. Zhang, Y. Song, and D. Liu, "A low cost flexible digital twin platform for spacecraft lithium-ion battery pack degradation assessment," in 2019 IEEE International Instrumentation and measurement technology conference (I2MTC), 2019: IEEE, pp. 1-6. https://doi.org/10.1109/I2MTC.2019.8827160
  50. L. Merkle, AS. Segura, JT. Grummel, and M. Lienkamp, "Architecture of a digital twin for enabling digital services for battery systems," in 2019 IEEE international conference on industrial cyber physical systems (ICPS), 2019: IEEE, pp. 155-160. https://doi.org/10.1109/ICPHYS.2019.8780347
  51. Z. Li et al., "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied energy, vol. 164, pp. 284-293, 2016. https://doi.org/10.1016/j.apenergy.2015.11.060
  52. B. Wang, G. Zhang, H. Wang, J. Xuan, and K. Jiao, "Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model," Energy and AI, vol. 1, p. 100004, 2020. https://doi.org/10.1016/j.egyai.2020.100004
  53. J. Zhao and J. Zhu, "A digital twin approach for fault diagnosis in PEM fuel cell systems," in 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), 2021: IEEE, pp. 168-171. https://doi.org/10.1109/DTPI52967.2021.9540157
  54. P. Costamagna et al., "Fault diagnosis strategies for SOFC-based power generation plants," Sensors, vol. 16, no. 8, p. 1336, 2016. https://doi.org/10.3390/s16081336
  55. JL. Kang, CC. Wang, DSH. Wong, SS. Jang, and CH. Wang, "Digital twin model and dynamic operation for a plant-scale solid oxide fuel cell system," Journal of the Taiwan Institute of Chemical Engineers, vol. 118, pp. 60-67, 2021. https://doi.org/10.1016/j.jtice.2021.01.001
  56. NP. Preve and EN. Protonotarios, "An integrated sensor web grid cyberimplementation for environmental protection," IEEE Sensors Journal, vol. 11, no. 9, pp. 1787-1794, 2011. https://doi.org/10.1109/JSEN.2011.2104949
  57. G. Li et al., "Energy efficient data collection in large-scale internet of things via computation offloading," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4176-4187, 2018. https://doi.org/10.1109/JIOT.2018.2875244
  58. S. Boschert, C. Heinrich, and R. Rosen, "Next generation digital twin," in Proc. tmce, 2018, vol. 2018: Las Palmas de Gran Canaria, Spain, pp. 7-11.