access icon free Comparison of different regression models to estimate fault location on hybrid power systems

© The Institution of Engineering and Technology
Received 22/07/2018, Accepted 03/09/2019, Revised 07/07/2019, Published 04/09/2019

Various pattern recognition methods have been suggested for estimating high-voltage alternating current transmission line fault location. However, insufficient studies have been conducted on the transmission lines connected to hybrid power generation systems such as wind and solar plants. In this study, the performance of different regression methods was investigated on a hybrid power system. Different faults with random distances on the transmission line were simulated and a fault database created by recording the current and voltage signals of these faults. After normalising this data in the pre-processing phase, it was passed to the digital signal processing stage. By repeating the experiments, 497 different faults were created. Fault types, fault resistances, and fault inception angles were changed randomly in order to obtain similar fault occurrence conditions as in real life by writing a Matlab code. In order to obtain distinctive features, the discrete wavelet transform was used. For training and validation of the dataset, Matlab Regression Learner App (RLA) was employed and the obtained results compared to select the best model. After significant fault simulation, Matern 5/2, a type of Gaussian progress regression model, showed more promising results compared to other RLA models.

Inspec keywords: discrete wavelet transforms; pattern recognition; power transmission lines; fault location; fault diagnosis; wavelet transforms; power transmission faults; hybrid power systems; regression analysis

Other keywords: 497 different faults; fault types; Matlab Regression Learner App; similar fault occurrence conditions; current voltage signals; pattern recognition methods; digital signal processing stage; fault database; fault resistances; pre-processing phase; hybrid power system; different regression models; significant fault simulation; different regression methods; fault inception angles; current transmission line fault location; Gaussian progress regression model; insufficient studies; hybrid power generation systems

Subjects: Integral transforms; Other topics in statistics; Power engineering computing; Other topics in statistics

References

    1. 1)
      • 1. Sekuçoğlu, A.S.: ‘Fotovoltaik (PV), Rüzgâr ve Hibrit Sistemlerin Tasarımı ve Ekonomik Analizi’. Master thesis, Black Sea Technical University, 2012.
    2. 2)
      • 27. Jia, K., Bi, T., Ren, Z., et al: ‘High frequency impedance based fault location in distribution system with DGs’, IEEE Trans. Smart Grid, 2016, 9, (2), pp. 807–816.
    3. 3)
      • 21. Manjusree, Y., Goli, R.K.: ‘Fault detection of a hybrid energy source integration with multiterminal transmission line using wavelet analysis’. 2017 Int. Conf. on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), Warangal, India, July 2017, pp. 120–126.
    4. 4)
      • 5. Rangari, C., Yadav, A.: ‘A hybrid wavelet singular entropy and fuzzy system based fault detection and classification on distribution line with distributed generation’. 2nd IEEE Int. Conf. on Recent Trends in Electronics, Information and Communication Technology, Proc., Bangalore, India, January 2018, pp. 1473–1477.
    5. 5)
      • 14. Hamidi, R.J., Livani, H.: ‘A travelling wave-based fault location method for hybrid three-terminal circuits’. IEEE Power and Energy Society General Meeting, Denver, CO, USA, October 2015, pp. 1–5.
    6. 6)
      • 4. Hashim, M.N., Osman, M.K., Ibrahim, M.N., et al: ‘Investigation of features extraction condition for impedance-based fault location in transmission lines’. 7th IEEE Int. Conf. on Control System, Computing and Engineering, Penang, Malaysia, February 2017, pp. 24–26.
    7. 7)
      • 19. Brahma, S.M.: ‘Fault location in power distribution system with penetration of distributed generation’, IEEE Trans. Power Deliv., 2011, 26, (3), pp. 1545–1553.
    8. 8)
      • 9. Sree, Y.M., Ravi, G., Shaik, A.G.: ‘Multi-terminal transmission line protection using wavelet based digital relay in the presence of wind energy source’. Int. Conf. on Electrical, Electronics, and Optimization Techniques, Chennai, India, March 2016, pp. 4124–4128.
    9. 9)
      • 29. Telukunta, V., Pradhan, J., Agrawal, A., et al: ‘Protection challenges under bulk penetration of renewable energy resources in power systems: A review’, CSEE J. Power Energy Syst., 2017, 3, (4), pp. 365–379.
    10. 10)
      • 33. Daubechies, I.: ‘Ten lectures of wavelets’ (CBMS-NSF regional conference series in applied mathematics, Vol 61, 1992).
    11. 11)
      • 15. Karajgi, S. B., Udaykumar, R. Y., Kamalapur, G. D.: ‘Fault location estimation in power distribution systems with high penetration of distributed generation’, Int. J. Comput. Electr. Eng., 2012, 4, (5), pp. 632–636.
    12. 12)
      • 36. Amin, S., Singhal, A.: ‘Identification and classification of neuro-degenerative diseases using feature selection through PCA-LD’. 4th IEEE Uttar Pradesh Section Int. Conf. on Electrical, Computer and Electronics, Mathura, India, October 2017, pp. 578–586.
    13. 13)
      • 30. Gururajapathy, S.S., Mokhlis, H., Illias, H.A.: ‘Fault location and detection techniques in power distribution systems with distributed generation: a review’, Renew. Sustain. Energy Rev., 2017, 74, pp. 949–958.
    14. 14)
      • 17. Panigrahi, B.K., Nanda, R.P., Nayak, A., et al: ‘Location of faults on a hybrid power system using impedance and wavelet transform method’. 2018 Second Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, June 2018, pp. 967–971.
    15. 15)
      • 18. Alwash, S.F., Ramachandaramurthy, V. K., Mithulananthan, N.: ‘Fault-location scheme for power distribution system with distributed generation’, IEEE Trans. Power Deliv., 2014, 30, (3), pp. 1187–1195.
    16. 16)
      • 34. Sharma, P., Saini, D., Saxena, A.: ‘Fault detection and classification in transmission line using wavelet transform and ANN’, Bull. Electr. Eng. Inf., 2016, 5, (3), pp. 284–295.
    17. 17)
      • 13. Hashim, M.N., Osman, M.K., Ibrahim, M.N., et al: ‘Single-ended fault location for transmission lines using traveling wave and multilayer perceptron network’. 6th IEEE Int. Conf. on Control System, Computing and Engineering (ICCSCE), Batu Ferringhi, Malaysia, November 2016, pp. 522–527.
    18. 18)
      • 31. Venkatesh, K.M.: ‘Hybrid photovoltaic and wind power system’ https://ch.mathworks.com/matlabcentral/fileexchange, accessed October 2019.
    19. 19)
      • 16. Dashti, R., Ghasemi, M., Daisy, M.: ‘Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model’, Energy, 2018, 159, pp. 344–360.
    20. 20)
      • 24. Lala, H., Karmakar, S., Ganguly, S.: ‘Detection and localization of faults in smart hybrid distributed generation systems: a Stockwell transform and artificial neural network-based approach’, Int. Trans. Electr. Energy Syst., 2019, 29, (2), pp. 1–18.
    21. 21)
      • 11. Chen, Y.Q., Member, S., Fink, O., et al: ‘Power transmission lines fault diagnosis with integrated feature extraction’, IEEE Trans. Ind. Electron., 2018, 65, (1), pp. 561–569.
    22. 22)
      • 12. Shafiullah, M., Abido, M.A., Al-Hamouz, Z.: ‘Wavelet-based extreme learning machine for distribution grid fault location’, IET Gener. Transm. Distrib., 2017, 11, (17), pp. 4256–4263.
    23. 23)
      • 22. Lucas, F., Costa, P., Batalha, R.M., et al: ‘High impedance fault detection in time-varying distributed generation systems using adaptive neural networks’. 2018 Int. Joint Conf. on Neural Networks (IJCNN), Rio de Janeiro, Brazil, October 2018, pp. 1–8.
    24. 24)
      • 28. Unal, F., Ekici, S.: ‘A fault location technique for HVDC transmission lines using extreme learning machines’. 2017 5th Int. Istanbul Smart Grids and Cities Congress and Fair (ICSG), Istanbul, Turkey, April 2017, pp. 125–129.
    25. 25)
      • 6. Gururaja Rao, H. V., Mala, R.C., Karanam, C.K.S., et al: ‘Detection, classification and location of overhead line faults using wavelet transform’, Indian J. Sci. Technol., 2017, 10, (3), pp. 1–6.
    26. 26)
      • 10. Sree, Y.M., Goli, R.K., Priya, G.V., et al: ‘A four terminal transmission line protection by wavelet approach in the presence of SVC using hybrid generation’. 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017, Vellore, India, April 2017, pp. 1–6.
    27. 27)
      • 25. Dehghani, F., Khodnia, F., Dehghan, E.: ‘Fault location of unbalanced power distribution feeder with distributed generation using neural networks’, CIRED-Open Access Proc. J., 2017, 2017, (1), pp. 1134–1137.
    28. 28)
      • 20. Zhou, Q., Zheng, B., Wang, C., et al: ‘Fault location for distribution networks with distributed generation sources using a hybrid DE/PSO algorithm’. IEEE Power & Energy Society General Meeting, Vancouver, Canada, July 2013, pp. 1–5.
    29. 29)
      • 32. Lai, T.M., Snider, L.A., Lo, E., et al: ‘High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion’, IEEE Trans. Power Deliv., 2005, 20, (1), pp. 397–407.
    30. 30)
      • 3. Nandi, R., Panigrahi, B.K.: ‘Detection of fault in a hybrid power system using wavelet transform’. Michael Faraday IET Int. Summit: MFIIS-2015, Kolkata, India, July 2015, pp. 203–206.
    31. 31)
      • 7. Ekici, S.: ‘Elektrik güç sistemlerinde akilli sistemler yardimiyla ariza tipi ve yerinin belirlenmesi’. PhD thesis, Firat University, 2007.
    32. 32)
      • 2. Kaur, M., Singh, V.: ‘Design and analysis of hybrid power system with fault detection and removal capability in transmission line’, Int. J. Curr. Eng. Technol., 2015, 5, (5), pp. 3415–3417.
    33. 33)
      • 35. MathWorks: ‘Statistics and machine learning toolbox: user's guide’, 2017.
    34. 34)
      • 8. Ray, P., Mishra, D.: ‘Artificial intelligence based fault location in a distribution system’. 2014 Int. Conf. on Information Technology, Bhubaneswar, India, December 2014, pp. 18–23.
    35. 35)
      • 23. James, J. Q., Hou, Y., Lam, A. Y., et al: ‘Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks’, IEEE Trans. Smart Grid, 2017, 10, (2), pp. 1694–1703.
    36. 36)
      • 26. Mora-Flórez, J.J., Herrera-Orozco, R.A., Bedoya-Cadena, A.F.: ‘Fault location considering load uncertainty and distributed generation in power distribution systems’, IET Gener. Transm. Distrib., 2015, 9, (3), pp. 287–295.
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