The fault classification of medium-voltage transmission lines consisting of wind farms connected to the high-voltage (HV) transmission networks is carried out using the power-spectrum-based hyperbolic S-transform (HST) powerful technique for analysing non-linear and non-stationary fault signals through the non-intrusive monitoring systems. The HST technique extracts the useful features in the time-frequency domain from measuring fault current waveforms of the HV utility side to discriminate the fault types. Parseval's theorem is applied to each HST coefficient to quantify the energy distribution of various fault types for reducing the size of inputs for recognition algorithms. Next, multiclass support vector machines achieve identification. The results have proved that the proposed classification technique is independent of fault resistance, source impedance, and fault inception angles. Extensive simulations are conducted using the electromagnetic transients program to show that the recognition accuracy of the fault classification for all types is up to 96.84%.

References

1. 1)
  - 29. Babu, N.R., Mohan, B.J.: ‘Fault classification in power systems using EMD and SVM’, Ain Shams Eng. J., 2017, 8, (2), pp. 103–111.
2. 2)
  - 33. Samantaray, S.R., Dash, P.K., Panda, G.: ‘Fault classification and location using HS-transform and radial basis function neural network’, Electr. Power Syst. Res., 2006, 76, (9-10), pp. 897–905.
3. 3)
  - 25. Stockwell, R.G., Mansinha, L., Lowe, R.P.: ‘Localization of the complex spectrum: the S transform’, IEEE Trans. Signal Process., 1996, 44, (4), pp. 998–1001.
4. 4)
  - 4. Deng, F., Chen, Z.: ‘Operation and control of a dc-grid offshore wind farm under dc transmission system faults’, IEEE Trans. Power Deliv., 2013, 28, (3), pp. 1356–1363.
5. 5)
  - 20. Chang, H.H., Lian, K.L., Su, Y.C., et al: ‘Power-spectrum based wavelet transform for non-intrusive demand monitoring and load identification’, IEEE Trans. Ind. Appl., 2014, 50, (3), pp. 2081–2089.
6. 6)
  - 7. Niaki, S.H.A., Hosseini, S.M., Abdoos, A.A.: ‘Fault detection of HVDC cable in multi-terminal offshore wind farms using transient sheath voltage’, IET Renew. Power Gener., 2017, 11, (13), pp. 1707–1713.
7. 7)
  - 28. Hsu, C.W., Lin, C.J.: ‘A comparison of methods for multiclass support vector machines’, IEEE Trans. Neural Netw., 2002, 13, (2), pp. 415–425.
8. 8)
  - 5. Mahfouz Aly, M.M.A., El-Sayed, M.A.H.: ‘Enhanced fault location algorithm for smart grid containing wind farm using wireless communication facilities’, IET Gener. Transm. Distrib., 2016, 10, (9), pp. 2231–2239.
9. 9)
  - 19. Asres, M.W., Girmay, A.A., Camarda, C., et al: ‘Non-intrusive load composition estimation from aggregate ZIP load models using machine learning’, Int. J. Electr. Power Energy Syst., 2019, 105, pp. 191–200.
10. 10)
  - 1. Hooshyar, A., Azzouz, M.A., El-Saadany, E.F.: ‘Distance protection of lines connected to induction generator-based wind farms during balanced faults’, IEEE Trans. Sustain. Energy, 2014, 5, (4), pp. 1193–1203.
11. 11)
  - 9. Perveen, R., Kishor, N., Mohanty, S.R.: ‘Fault detection for offshore wind farm connected to onshore grid via voltage source converter-high voltage direct current’, IET Gener. Transm. Distrib., 2015, 9, (16), pp. 2544–2554.
12. 12)
  - 30. Chang, C.C., Lin, C.J.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), p. 27.
13. 13)
  - 17. Hart, G.W.: ‘Non-intrusive appliance load monitoring’, Proc. IEEE, 1992, 80, (12), pp. 1870–1891.
14. 14)
  - 11. Prakash, T., Mohanty, S.R., Singh, V.P.: ‘Distance relaying algorithm for phasor measurement unit assisted zone-3 relays of series compensated wind integrated system’, IET Gener. Transm. Distrib., 2019, 13, (21), pp. 4788–4797.
15. 15)
  - 34. ‘Fuse selection guide’, OptiFuse – Rev. A, 2010.
16. 16)
  - 3. Ma, S.K., Geng, H., Liu, L., et al: ‘Grid-synchronization stability improvement of large-scale wind farm during severe grid fault’, IEEE Trans. Power Syst., 2018, 33, (1), pp. 216–225.
17. 17)
  - 16. Biswas, S., Nayak, P.K.: ‘A fault detection and classification scheme for unified power flow controller compensated transmission lines connecting wind farms’, IEEE Syst. J., 2020, pp. 1–10, early access.
18. 18)
  - 12. Vanfretti, L., Baudette, M., Domínguez-García, J., et al: ‘A phasor measurement unit based fast real-time oscillation detection application for monitoring wind-farm-to-grid sub-synchronous dynamics’, Electr. Power Compon. Syst., 2016, 44, (2), pp. 123–134.
19. 19)
  - 2. Ramtharan, G., Arulampalam, A., Ekanayake, J.B., et al: ‘Fault ride through of fully rated converter wind turbines with AC and DC transmission systems’, IET Renew. Power Gener., 2009, 3, (4), pp. 426–438.
20. 20)
  - 6. Yao, J., Guo, L., Zhou, T., et al: ‘Capacity configuration and coordinated operation of a hybrid wind farm with FSIG-based and PMSG-based wind farms during grid faults’, IEEE Trans. Energy Convers., 2017, 32, (3), pp. 1188–1199.
21. 21)
  - 14. Pradhan, A.K., Joos, G.: ‘Adaptive distance relay setting for lines connecting wind farms’, IEEE Trans. Energy Convers., 2007, 22, (1), pp. 206–213.
22. 22)
  - 10. Rather, Z.H., Chen, Z., Thøgersen, P., et al: ‘Pragmatic approach for multistage phasor measurement unit placement: a case study of the Danish power system and inputs from practical experience’, Int. Trans. Electr. Energy, 2016, 26, (12), pp. 2532–2551.
23. 23)
  - 26. Ashrafian, A., Rostami, M., Gharehpetian, G.B.: ‘Hyperbolic S-transform-based method for classification of external faults, incipient faults, inrush currents and internal faults in power transformers’, IET Gener. Transm. Distrib., 2012, 6, (10), pp. 940–950.
24. 24)
  - 8. Yang, J., Fletcher, J.E., O‘Reilly, J.: ‘Multi-terminal DC wind farm collection grid internal fault analysis and protection design’, IEEE Trans. Power Deliv., 2010, 25, (4), pp. 2308–2318.
25. 25)
  - 13. Rolán, A., Pedra, J.: ‘Initialization of DFIG wind turbines with a phasor-based approach’, Wind Energy, 2019, 22, (3), pp. 420–432.
26. 26)
  - 24. Chang, H.H.: ‘Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses’, Energies, 2012, 5, (11), pp. 4569–4589.
27. 27)
  - 21. Chang, H.H.: ‘Non-intrusive fault identification of power distribution systems in intelligent buildings based on power-spectrum-based wavelet transform’, Energy Build., 2016, 127, pp. 930–941.
28. 28)
  - 22. Chang, H.H., Linh, N.V.: ‘Statistical feature extraction for fault locations in non-intrusive fault detection of low voltage distribution systems’, Energies, 2017, 10, (5), p. 611.
29. 29)
  - 15. Nayak, P.K., Pradhan, A.K., Bajpai, P.: ‘Wide-area measurement-based backup protection for power network with series compensation’, IEEE Trans. Power Deliv., 2014, 29, (4), pp. 1970–1977.
30. 30)
  - 31. Fernandez-Delgado, M., Cernadas, E., Barro, S., et al: ‘Do we need hundreds of classifiers to solve real world classification problems?’, J. Mach. Learn. Res., 2014, 15, (1), pp. 3133–3181.
31. 31)
  - 18. Chang, H.H., Chen, K.L., Tsai, Y.P., et al: ‘A new measurement method for power signatures of non-intrusive demand monitoring and load identification’, IEEE Trans. Ind. Appl., 2012, 48, (2), pp. 764–771.
32. 32)
  - 23. Chang, H.H., Linh, N.V., Lee, W.J.: ‘A novel nonintrusive fault identification for power transmission networks using power-spectrum-based hyperbolic S-transform—part I: fault classification’, IEEE Trans. Ind. Appl., 2018, 54, (6), pp. 5700–5710.
33. 33)
  - 32. ‘EMTP Reference Models for Transmission Line Relay Testing, IEEE PES-PSRC’. Available at http://www.pes-psrc.org/Reports/EMTP%20Ref%20Model-Final.pdf.
34. 34)
  - 27. Cortes, C., Vapnik, V.: ‘Support-vector network’, Mach. Learn., 1995, 20, (3), pp. 273–297.

Fault classifications of MV transmission lines connected to wind farms using non-intrusive fault monitoring techniques on HV utility side

References

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