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Amirioun, M.H., Heydarian-Forushani, E.. (1403). A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation. سامانه مدیریت نشریات علمی, 12(2), 175-185. doi: 10.22098/joape.2023.11168.1833
M.H. Amirioun; E. Heydarian-Forushani. "A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation". سامانه مدیریت نشریات علمی, 12, 2, 1403, 175-185. doi: 10.22098/joape.2023.11168.1833
Amirioun, M.H., Heydarian-Forushani, E.. (1403). 'A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation', سامانه مدیریت نشریات علمی, 12(2), pp. 175-185. doi: 10.22098/joape.2023.11168.1833
Amirioun, M.H., Heydarian-Forushani, E.. A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation. سامانه مدیریت نشریات علمی, 1403; 12(2): 175-185. doi: 10.22098/joape.2023.11168.1833

A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation

Journal of Operation and Automation in Power Engineering
مقاله 8، دوره 12، شماره 2، تیر 2024، صفحه 175-185    اصل مقاله (1.71 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.11168.1833
نویسندگان
M.H. Amirioun* 1؛ E. Heydarian-Forushani2
1Department of Electrical Engineering, Shahreza Campus, University of Isfahan, Iran
2Department of Electrical and Computer Engineering, Qom University of Technology, Qom, 1519-37195, Iran
چکیده
With the advent of advanced measurement and supervisory devices in power systems, wide area measurement systems and real-time monitoring of power systems have become viable. Accordingly, modeling techniques should be updated as well. This paper proposes a transformer asset management model based on real-time condition monitoring in the presence of distributed generation. The model is tested under different case studies and compared with the previous models in which constant failure rate model was used for asset management of transformers. The system cost includes operation, repair, and interruption costs. The objective is to determine the hourly loading of the transformer so that the cost of system is minimized. The long-term objective is to determine the loading pattern of the transformer which guaranties the most economical pattern among various options. Results showed that the proposed model is efficiently capable of returning more accurate responses if real-time monitoring data is used. A set of sensitivity analysis studies are also performed to highlight the impact of each factor separately. The contribution of distributed generators to supply the load is also investigated. Results showed that the use of distributed generators reduces the overall cost of the system by diminishing the risk-based element of the system cost.
کلیدواژه‌ها
Transformer asset management؛ Real-time condition monitoring؛ Risk؛ Distributed generation
مراجع
  1. A. Hosseini, M. Hajiaghapour-Moghimi, E. Hajipour, and M. Vakilian, “Advanced distribution transformer asset management embracing its relocation: A cost saving approach,” IET Gener. Transm. Distrib., vol. 16, no. 12, pp. 2370–2385, 2022.
  2. -S. Hwang, S.-D. Mun, T.-J. Kim, G.-W. Oh, Y.-S. Sim, and S. J. Chang, “Development of Data Cleaning and Integration Algorithm for Asset Management of Power System,” Energies, vol. 15, no. 5, p. 1616, 2022.
  3. Behjat, A. Shams, and V. Tamjidi, “Characterization of power transformer electromagnetic forces affected by winding faults,” J. Oper. Autom. Power Eng., vol. 6, no. 1, pp. 40–49, 2018.
  4. Behkam, A. Moradzadeh, H. Karimi, M. S. Nadery, B. Mohammadi Ivatloo, G. B. Gharehpetian, and S. Tenbohlen, "Mechanical Fault Types Detection in Transformer Windings Using Interpretation of Frequency Responses via Multilayer Perceptron," J. Oper. Autom. Power Eng., vol. 11, no. 1, p. 11-21, 2023.
  5. S. M. Gómez and J. R. García, “Analysis of Asset Management Models for a Transformer Fleet in the National Laboratory of Smart Grids (LAB+ i),” Trans. Energy Syst. Eng. Appl., vol. 3, no. 1, pp. 7–12, 2022.
  6. Yang,        W.             Chen,        F.               Wan,        Y.               Zhou,        and           J.        Wang, “Identification of the aging stage of transformer oil-paper insulation via Raman spectroscopic characteristics,” IEEE Trans. Dielectr. Electr. Insul., vol. 27, no. 6, pp. 1770–1777, 2020.
  7. H. Mashhadzadeh, M. Ghanbari, A. Koochaki, and M. G. Ahangari, “Improving the Thermal and Electrical Properties of Transformer Oil Using Hybrid Nanofluid„” J. Oper. Autom. Power Eng., vol. 10, no. 3, pp. 175–178, 2022.
  8. Liu, X. Fan, Y. Zhang, C. Zhang, and Z. Wang, “Aging evaluation and moisture prediction of oil-immersed cellulose insulation in field transformer using frequency domain spectroscopy and aging kinetics model,” Cellulose, vol. 27, no. 12, pp. 7175–7189, 2020.
  9. Kaliappan and M. Rengaraj, “Aging assessment of transformer solid insulation: A review,” Mater. Today Proc., vol. 47, pp. 272–277, 2021.
  10. Jin, D. Kim, A. Abu-Siada, and S. Kumar, “Oil-Immersed Power Transformer Condition Monitor-ing Methodologies: A Review,” Energies, vol. 15, no. 9, p. 3379, 2022.
  11. Moravej and S. Bagheri, “Condition monitoring techniques of power transformers: A review,” J. Oper. Autom. power Eng., vol. 3, no. 1, pp. 71–82, 2015.
  12. Aminifar, M. Abedini, T. Amraee, P. Jafarian, M. H. Samimi, and M. Shahidehpour, “A review of power system protection and asset management with machine learning techniques,” Energy Syst., pp. 1–38, 2021.
  13. P. Sharma, “Regression approach to power transformer health assessment using health index,” Advances in Electromechanical Technologies, Springer, pp. 603–616, 2021.
  14. Li, A. Zhang, J. Huang, and Z. Xu, “An Approach Based on Transfer Learning to Lifetime Deg-radation Rate Prediction of the Dry-Type Transformer,” IEEE Trans. Ind. Electron., 2022.
  15. Zhang, Z. Chu, H. Guo, and B. Wang, “Fuzzy Failure Rate Model of Power Transformer Based on Condition Monitoring,” 5th Int. Conf. Energy Elec. Power Eng. (CEEPE), 2022, pp. 142–149.
  16. Forouhari and A. Abu-Siada, “Application of adaptive neuro fuzzy inference system to support power transformer life estimation and asset management decision,” IEEE Trans. Dielectr. Electr. In-sul., vol. 25, no. 3, pp. 845–852, 2018.
  17. Padmanaban, M. Khalili, M. A. Nasab, M. Zand, A. G. Shamim, and B. Khan, “Determination of Power Transformers Health Index Using Parameters Affecting the Transformer’s Life,” IETE J. Res., pp. 1–22, 2022.
  18. D. Medina, D. A. Zaldivar, A. A. Romero, J. Zuñiga, and E. E. Mombello, “A fuzzy infer-ence-based approach for estimating power transformers risk index,” Electr. Power Syst. Res., vol. 209, p. 108004, 2022.
  19. Sun, L. Yang, F. Zare, Y. Lin, and Z. Cheng, “Improved method for aging assessment of winding hot-spot insulation of transformer based on the 2-FAL concentration in oil,” Int. J. Electr. Power Energy Syst., vol. 112, pp. 191–198, 2019.
  20. E. Gouda, G. M. Amer, and W. A. A. Salem, “Predicting transformer temperature rise and loss of life in the presence of harmonic load currents,” Ain Shams Eng. J., vol. 3, no. 2, pp. 113–121, 2012.
  21. Wang, L. Zhou, Z.X. Yang, Y. Cui, L. Wang, J. Jiang, and L. Guo, “A new testing method for the dielectric response of oil-immersed transformer,” IEEE Trans. Ind. Electron., vol. 67, no. 12, pp. 10833–10843, 2019.
  22. Abiri-Jahromi, M. Parvania, F. Bouffard, and M. Fotuhi-Firuzabad, “A two-stage framework for power transformer asset maintenance management—Part I: Models and formulations,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1395–1403, 2012.
  23. Abiri-Jahromi, M. Parvania, F. Bouffard, and M. FotuhiFiruzabad, “A two-stage framework for power transformer asset maintenance management—Part II: Validation results,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1404–1414, 2012.
  24. Ning, W. Wu, B. Zhang, and P. Zhang, “A timevarying transformer outage model for on-line op-erational risk assessment,” Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 600–607, 2011.
  25. E. Gouda, S. H. El-Hoshy, and H. H. EL-Tamaly, “Condition assessment of power transformers based on dissolved gas analysis,” IET Gener. Transm. Distrib., vol. 13, no. 12, pp. 2299–2310, 2019.
  26. Soni and B. Mehta, “Review on asset management of power transformer by diagnosing incipient faults and faults identification using various testing methodologies,” Eng. Fail. Anal., vol. 128, p. 105634, 2021.
  27. Murugan and R. Raju, “Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities,” Int. J. Syst. Assur. Eng. Manag., vol. 12, no. 2, pp. 318–336, 2021.
  28. G. T. Da Silva, H. J. B. Da Silva, F. P. Marafão, H. K. M. Paredes, and F. A. S. Gonçalves, “En-hanced health index for power transformers diagnosis,” Eng. Fail. Anal., vol. 126, p. 105427, 2021.
  29. F. Al Hamdani, R. A. Prasojo, and A. Abu-Siada, “Power transformer degradation condition and insulation index estimation based on historical oil dat,” 2nd Int. Conf. High Voltage Eng. Power Syst. (ICHVEPS), 2019, pp. 1–5.
  30. Guo and L. Guo, “Health index for power transformer condition assessment based on operation history and test data,” Energy Reports, vol. 8, pp. 9038–9045, 2022.
  31. Liu, I. Dassios, G. Tzounas, and F. Milano, “Stability analysis of power systems with inclusion of realistic-modeling WAMS delays,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 627–636, 2018.
  32. G. Phadke and T. Bi, “Phasor measurement units, WAMS, and their applications in protection and control of power systems,” J. Mod. Power Syst. Clean Energy, vol. 6, no. 4, pp. 619–629, 2018.
  33. Dubey, M. Popov, and J. de J. C. Muro, “Cost effective wide area measurement systems for smart power network,” IEEE Power Energy Technol. Syst. J., vol. 5, no. 3, pp. 85–93, 2018.
  34. Suwnansri, “Asset management of power transformer: Optimization of operation and maintenance costs,” Int. Elec. Eng. Cong. (iEECON), pp. 1–4, 2014.
  35. Weekes, T. Molinski, and G. Swift, “Transient transformer overload ratings and protection,” IEEE Electr. Insul. Mag., vol. 20, no. 2, pp. 32–35, 2004.
  36. Biçen, Y. Çilliyüz, F. Aras, and G. Aydugan, “An assessment on aging model of IEEE/IEC standards for natural and mineral oil-immersed transformer,” IEEE Int. Conf. Dielectric Liquids, pp. 1–4, 2011.
  37. S. Dhillon, Engineering systems reliability, safety, and maintenance: an integrated approach. CRC Press, 2017.
  38. F. Kovalev and L. M. Lebedeva, Reliability of power systems, vol. 1. Springer, 2019.
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