آمار

تعداد نشریات30
تعداد شماره‌ها414
تعداد مقالات3,654
تعداد مشاهده مقاله5,765,925
تعداد دریافت فایل اصل مقاله3,945,134
Venkata, P., Pandya, V., Sant, A.V.. (1402). Data Mining Model Based Differential Microgrid Fault Classification Using SVM ‎Considering Voltage and Current Distortions. سامانه مدیریت نشریات علمی, 11(3), 162-172. doi: 10.22098/joape.2023.10185.1722
P. Venkata; V. Pandya; A.V. Sant. "Data Mining Model Based Differential Microgrid Fault Classification Using SVM ‎Considering Voltage and Current Distortions". سامانه مدیریت نشریات علمی, 11, 3, 1402, 162-172. doi: 10.22098/joape.2023.10185.1722
Venkata, P., Pandya, V., Sant, A.V.. (1402). 'Data Mining Model Based Differential Microgrid Fault Classification Using SVM ‎Considering Voltage and Current Distortions', سامانه مدیریت نشریات علمی, 11(3), pp. 162-172. doi: 10.22098/joape.2023.10185.1722
Venkata, P., Pandya, V., Sant, A.V.. Data Mining Model Based Differential Microgrid Fault Classification Using SVM ‎Considering Voltage and Current Distortions. سامانه مدیریت نشریات علمی, 1402; 11(3): 162-172. doi: 10.22098/joape.2023.10185.1722

Data Mining Model Based Differential Microgrid Fault Classification Using SVM ‎Considering Voltage and Current Distortions

Journal of Operation and Automation in Power Engineering
دوره 11، شماره 3، دی 2023، صفحه 162-172    اصل مقاله (784.61 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10185.1722
نویسندگان
P. Venkata* ؛ V. Pandya؛ A.V. Sant
Electrical Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinatar, ‎Gujarat, India
چکیده
This paper reports support vector machine (SVM) based fault detection and classification in microgrid while considering distortions in voltages and currents, time and frequency series parameters, and differential parameters. For SVM-based fault classification, the data set is formed by analysing the operation of the standard IEC microgrid model, with and without grid interconnection, under different fault and non-fault scenarios. Fault scenarios also include different locations, resistances, and incident angles of fault. Whereas, for non-fault scenarios, the variation in load is considered. Voltages and currents from both ends of the distribution line (DL) are sampled at 1920 Hz. The time and frequency series parameters, total harmonic distortion (THD) in current and voltage, and differential parameters are determined. The SVM algorithm uses these parameters to detect and classify faults. The performance of this developed SVM based algorithm is compared with that of different machine learning algorithms. This comparative analysis reveals that SVM detects and classifies the faults on the microgrid with an accuracy of over 99.99%. The performance of the proposed method is also tested with 30 dB, 35 dB, and 40 dB noise in the generated data, which represent measurement errors.
کلیدواژه‌ها
Data Mining؛ Fault Identification and Classification؛ Microgrid Protection؛ Machine Learning؛ SVM.‎
مراجع
  1. Ahmad and D. Zhang, “A critical review of comparative global historical energy consumption and future demand: The story told so far”, Energy Rep., vol. 6, pp. 1973–1991, 2020.
  2. J. Warner and G. A. Jones, “The 21st century coal question: China, India, development, and climate change”, Atmosphere, vol. 10, pp. 476, 2019.
  3. V. Sant, V. Khadkikar, W. Xiao, H. Zeineldin and A Al-Hinai, “Adaptive control of grid connected photo voltaic inverter for maximum VA utilisation”, IECON 2013-39th Annual Conf. IEEE Ind. Electron. Soc., 2013.
  4. Kempener, E.  Assoumou, A. Chiodi, U. Ciorba, M. Gaeta, D. Gielen, H. Hamasaki, A. Kanudia, T. Kober, M. Labriet, et al, “A global renewable energy roadmap: Comparing energy systems models with irena’s remap 2030 project”, Inf. Energy Climate Policies Using Energy Syst. Models, 2015.
  5. Rai, N. D. Londhe and R. Raj, “Fault classification in power system distribution network integrated with distributed generators using CNN”, Electr. Power Syst. Res., vol. 192, pp. 106914, 2021.
  6. S. Bhalja, A.V. Sant, A. Markana and B. R. Bhalja, “Microgrid with five-level diode clamped inverter based hybrid generation system”, 2019 IEEE Int. Conf.  Electr. Comput. Communication Tech., 2019.
  7. H. Einaddin, A. S. Yazdankhah and R Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources”, J. Oper. Autom. Power Eng., vol. 5, pp. 1–10, 2017.
  8. Zolfaghari, G. B. Gharehpetian and M. Abedi, “A repetitive control-based approach for power sharing among boost converters in dc microgrids”, J. Oper. Autom. Power Eng., vol. 7, pp. 168–175, 2019.
  9. R. Aghajani and I. Heydari, “Energy management in microgrids containing electric vehicles and renewable energy sources considering demand response”, J. Oper. Autom. Power Eng., vol. 9, pp. 34–48, 2021.
  10. S. Zavareh, E. Rokrok, J. Soltani and M.R. Shahkarami, “Adaptive sliding mode control of multi-dg, multi-bus grid connected microgrid”, J. Oper. Autom. Power Eng., vol. 7, pp. 65–77, 2019.
  11. Ghobadpour, M. Gandomkar and J. Nikoukar, “Multi-objective function optimisation for locating and sizing of distributed generation sources in radial distribution networks with fuse and recloser protection”. J. Oper. Autom. Power Eng., vol. 9, pp. 266–273, 2021.
  12. El-Naily, S. M. Saad, T. Hussein, and F. A. Mohamed, “A novel constraint and non-standard characteristics for optimal over-current relays coordination to enhance microgrid protection scheme”, IET Gener. Transm. Distrib., vol. 13, pp. 780–793, 2019.
  13. M. Saad, N. El-Naily and F. A. Mohamed, “A new constraint considering maximum psm of industrial over-current relays to enhance the performance of the optimisation techniques for microgrid protection schemes”, Sustain. Cities Soc., vol. 44, pp. 445–457, 2019.
  14. M. Sharaf, H. H. Zeineldin and E. El-Saadany, “Protection coordination for microgrids with grid connected and islanded capabilities using communication assisted dual setting directional overcurrent relays” IEEE Trans. Smart Grid, vol.  9, pp. 143–151, 2016.
  15. Xyngi and M. Popov, “An intelligent algorithm for the protection of smart power systems”, IEEE Trans. Smart Grid, vol. 4, pp. 1541–1548, 2013.
  16. Lahiji, F. B. Ajaei and R. E. Boudreau, “Non-pilot protection of the inverter-dominated microgrid”, IEEE Access, vol. 7, pp. 142190–142202, 2019.
  17. Wang and L. Jing, “A protection method for inverter based microgrid using current-only polarity comparison”, J. Modern Power Syst. Clean Energy, vol. 8, pp. 446–53, 2019.
  18. Hong and M. T. A. M. Cabatac, “Fault detection, classification, and location by static switch in microgrids using wavelet transform and taguchi-based artificial neural network”, IEEE Syst. J., vol. 14(2), pp. 2725–2735, 2019.
  19. Jamali, S. Ranjbar and A. Bahmanyar, “Identification of faulted line section in microgrids using data mining method based on feature discretisation”, Int. Trans. Electr. Energy Syst., vol. 30(6), pp. e12353, 2020.
  20. R. Ola, A. Saraswat, S. K. Goyal, S.K. Jhajharia, B. Khan, O. P. Mahela, H. H. Alhelou and Pierluigi Siano, “A protection scheme for a power system with solar energy penetration”, Appl. Sci., vol. 10(4), pp. 1516, 2020.
  21. K. Rao, T. Gangwar and S. Sarangi, “Advanced relaying for dg-penetrated distribution system”, Arabian J. Sci. Eng., vol. 46(10), pp. 9649–9661, 2021.
  22. Baloch, S. Z. Jamali, K. K. Mehmood, S. B. A. Bukhari, M. S. U. Zaman, A. Hussain and C.H. Kim, “Microgrid protection strategy based on the autocorrelation of current envelopes using the squaring and low-pass filtering method”, Energies, vol. 3(9), pp. 2350, 2020.
  23. D. Mamuya, Y.D. Lee, J.W. Shen, M. Shafiullah and C. C. Kuo, ”Application of machine learning for fault classification and location in a radial distribution grid”, Appl. Sci., vol. 10(14), pp. 4965, 2020.
  24. S. Kumar, D. Srinivasan and T. Reindl, “A fast and scalable protection scheme for distribution networks with distributed generation”, IEEE Trans. Power Del., vol. 31(1), pp. 67–75, 2015.
  25. Q. James, Y. Hou, A.Y. S. Lam and V. O. K.  Li, “Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks”, IEEE Trans. Smart Grid, vol. 10(2), pp. 1694–1703, 2017.
  26. Aljohani, T. Sheikhoon, A. Fataa, M. Shafiullah and M.A. Abido, “Design and implementation of an intelligent single line to ground fault locator for distribution feeders”, 2019 Int. Conf. Control, Autom. Diagnosis (ICCAD), 2019.
  27. P. Mishra, S. R. Samantaray and Geza Joos, “A combined wavelet and data-mining based intelligent protection scheme for microgrid”, IEEE Trans. Smart Grid, vol. 7(5), pp. 2295–2304, 2015.
  28. Shafiullah, M. A. Abido and Z. A.L. Hamouz, “Wavelet-based extreme learning machine for distribution grid fault location”, IET Gener. Transm. Distrib., vol. 11(17), pp. 4256–4263, 2017.
  29. Baloch and M. S. Muhammad, “An intelligent data mining-based fault detection and classification strategy for microgrid”, IEEE Access, vol. 9, pp. 22470–22479, 2021.
  30. Kar, S.R. Samantaray and M. D. Zadeh, “Data-mining model based intelligent differential microgrid protection scheme”, IEEE Syst. J., vol. 11(2), pp. 1161–1169, 2015.
  31. Mishra and P. K. Rout, “Detection and classification of micro-grid faults based on HHT and machine learning techniques”, IET Gener. Transm. Distrib., vol. 12(2), pp. 388–397, 2018.
  32. Li, A. Dysko and G. M. Burt, “Traveling wave-based protection scheme for inverter-dominated microgrid using mathematical morphology”, IEEE Trans. Smart Grid, vol. 5(5), pp. 2211–2218, 2014.
  33. S. Oh, C.H. Kim, G.H. Gwon, C.H. Noh, S. B. A. Bukhari, R. Haider and T. Gush, “Fault detection scheme based on mathematical morphology in last mile radial low voltage dc distribution networks”, Int. J. Electr. Power Energy Syst., vol. 106, pp. 520–527, 2019.
  34. A. A. Hassan, A. Reiman, G. F. Reed, Z.H. Mao and B. M. Grainger, “Model-based fault detection of inverter-based microgrids and a mathematical framework to analyse and avoid nuisance tripping and blinding scenarios”, Energies, vol. 11(8), pp. 2152, 2018.
  35. Beder, B. Mohandes, M. S. E. Moursi, E.A. Badran, and M. M. E. Saadawi, “A new communication-free dual setting protection coordination of microgrid”, IEEE Trans. Power Del., vol. 36(4), pp. 2446–2458, 2020.
  36. Chen, X. Pei, M. Yang, L. Peng and P. Shi, “A novel protection scheme for inverter-interfaced microgrid (iim) operated in islanded mode”, IEEE Trans. Power Electron., vol. 33(9), pp. 7684–7697, 2017.
  37. F. Habib, M. M. Esfahani and O. Mohammed, “Development of protection scheme for active distribution systems with penetration of distributed generation”, SoutheastCon, 2018.
  38. Jain, D. L. Lubkeman and S. M. Lukic,” Dynamic adaptive protection for distribution systems in grid-connected and islanded modes”, IEEE Trans, Power Del., vol. 34(1), pp. 281–289, 2018.
  39. R. Fahim, S. K. Sarker, S.M. Muyeen, M. Sheikh, R. Islam, and S.K.Das, “Microgrid fault detection and classification: Machine learning based approach, comparison, and reviews”, Energies, vol. 13(13), pp. 3460, 2020.
  40. Nsengiyaremye, B. C. Pal and M. M. Begovic, “Microgrid protection using low-cost communication systems” IEEE Trans. Power Del., vol. 35(4), pp. 2011–2020, 2019.
  41. A. Jarrahi, H. Samet and T. Ghanbari, “Novel change detection and fault classification scheme for ac microgrids”, IEEE Syst. J., vol. 14(3), pp. 3987–3998, 2020.
  42. S. Noble, “What is a support vector machine?”, Nature biotechnology, vol. 24(12), pp. 1565–1567, 2006.
  43. Manitoba HVDC Research Centre. PSCAD/EMTDC, version: 4.5.0.0, 2012.
  44. V. Rossum. Python, version: 3.0, 2008.
  45. S. Abdelgayed, W. G. Morsi and T. S. Sidhu. “A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit”. IEEE Trans. Smart Grid, vol. 9(5), pp.4838–4846, 2017.
آمار
تعداد مشاهده مقاله: 974
تعداد دریافت فایل اصل مقاله: 1,010


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب