access icon free Approach to fitting parameters and clustering for characterising measured voltage dips based on two-dimensional polarisation ellipses

© The Institution of Engineering and Technology
Received 23/09/2016, Accepted 09/01/2017, Revised 04/12/2016, Published 01/02/2017

An alternative approach to characterise real voltage dips is proposed and evaluated in this study. The proposed methodology is based on voltage-space vector solutions, identifying parameters for ellipses trajectories by using the least-squares algorithm applied on a sliding window along the disturbance. The most likely patterns are then estimated through a clustering process based on the k-means algorithm. The objective is to offer an efficient and easily implemented alternative to characterise faults and visualise the most likely instantaneous phase-voltage evolution during events through their corresponding voltage-space vector trajectories. This novel solution minimises the data to be stored but maintains extensive information about the dips including starting and ending transients. The proposed methodology has been applied satisfactorily to real voltage dips obtained from intensive field-measurement campaigns carried out in a Spanish wind power plant up to a time period of several years. A comparison to traditional minimum root mean square-voltage and time-duration classifications is also included in this study.

Inspec keywords: vectors; least squares approximations; power supply quality; wind power plants; pattern clustering

Other keywords: voltage-space vector trajectories; least-squares algorithm; Spanish wind power plant; starting transients; intensive field-measurement campaigns; sliding window; power quality; time-duration classifications; ellipses trajectories; clustering process; ending transients; voltage dips; two-dimensional polarisation ellipses; minimum root mean square-voltage; k-means algorithm; fitting parameters; instantaneous phase-voltage evolution

Subjects: Power supply quality and harmonics; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Wind power plants

References

    1. 1)
      • 23. Alam, M.R., Muttaqi, K.M., Bouzerdoum, A.: ‘A new approach for classification and characterization of voltage dips and swells using 3-d polarization ellipse parameters’, IEEE Trans. Power Deliv., 2015, 30, (3), pp. 1344–1353.
    2. 2)
      • 27. Polajzer, B., Tumberger, G.S., Seme, S., et al: ‘Detection of voltage sag sources based on instantaneous voltage and current vectors and orthogonal Clarke's transformation’, IET Gener. Transm. Distrib., 2008, 2, (2), pp. 219–226.
    3. 3)
      • 24. Duan, R.C., Wang, F.H., Zhang, J., et al: ‘Data mining amp; pattern recognition of voltage sag based on k-means clustering algorithm’. 2015 IEEE Power Energy Society General Meeting, July 2015, pp. 1–5.
    4. 4)
      • 36. Honrubia-Escribano, A., Gómez-Lázaro, E., Molina-García, A., et al: ‘Load influence on the response of ac-contactors under power quality disturbances’, Int. J. Electr. Power Energy Syst., 2014, (63), pp. 846–854.
    5. 5)
      • 9. Bollen, M.H.J.: ‘Characterisation of voltage sags experienced by three-phase adjustable-speed drives’, IEEE Trans. Power Deliv., 1997, 12, (4), pp. 1666–1671.
    6. 6)
      • 20. Costa, F.B., Driesen, J.: ‘Assessment of voltage sag indices based on scaling and wavelet coefficient energy analysis’, IEEE Trans. Power Deliv., 2013, 28, (1), pp. 336–346.
    7. 7)
      • 10. Bollen, M.H., Zhang, L.: ‘Different methods for classification of three-phase unbalanced voltage dips due to faults’, Electr. Power Syst. Res., 2003, 66, (1), pp. 59–69.
    8. 8)
      • 12. Haghbin, M., Farjah, E., Mazaherifar, H.: ‘Improved power quality monitor placement using innovative indices’. 2013 4th Power Electronics, Drive Systems and Technologies Conf. (PEDSTC), February 2013, pp. 501–509.
    9. 9)
      • 18. Polajzer, B., Stumberger, G., Seme, S., et al: ‘Generalization of methods for voltage-sag source detection using vector-space approach’, IEEE Trans. Ind. Appl., 2009, 45, (6), pp. 2152–2161.
    10. 10)
      • 17. Barros, J., Perez, E.: ‘Limitations in the use of r.m.s. value in power quality analysis’. Proc. of the IEEE Instrumentation and Measurement Technology Conf., 2006. IMTC 2006, April 2006, pp. 2261–2264.
    11. 11)
      • 7. Mahela, O.P., Shaik, A.G., Gupta, N.: ‘A critical review of detection and classification of power quality events’, Renew. Sustain. Energy Rev., 2015, 41, pp. 495–505. Available at http://www.sciencedirect.com/science/article/pii/S1364032114007564.
    12. 12)
      • 29. Hair, J, ., Anderson, , R., Tatham, R., et al: ‘Multivariable data analysis’ (England, Prentice-Hall, 2009, 7th edn.).
    13. 13)
      • 2. Olguin, G., Vuinovich, F., Bollen, M.H.J.: ‘An optimal monitoring program for obtaining voltage sag system indexes’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 378–384.
    14. 14)
      • 25. Bollen, M.H.J., Gu, I.Y.H.: ‘On the analysis of voltage and current transients in three-phase power systems’, IEEE Trans. Power Deliv., 2007, 22, (2), pp. 1194–1201.
    15. 15)
      • 1. Wang, B., Xu, W., Pan, Z.: ‘Voltage sag state estimation for power distribution systems’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 806–812.
    16. 16)
      • 34. Bollen, M.: ‘Understanding power quality problems (voltage sags and interruptions)’ (Wiley-IEEE Press, New York, 2000).
    17. 17)
      • 19. Faiz, J., Lotfi-fard, S., Bollen, M.H.J.: ‘Wavelet-based Mann and Morrison algorithm for improvement of three-phase unbalanced voltage dips characterisation’, IET Gener. Transm. Distrib., 2007, 1, (4), pp. 640–646.
    18. 18)
      • 33. Guder, M., Salor, O., Cadirci, I., et al: ‘Data mining framework for power quality event characterization of iron and steel plants’, IEEE Trans. Ind. Appl., 2015, 51, (4), pp. 3521–3531.
    19. 19)
      • 35. Honrubia-Escribano, A., Gómez-Lázaro, E., Molina-García, A., et al: ‘Influence of voltage dips on industrial equipment: analysis and assessment’, Int. J. Electr. Power Energy Syst., 2012, 41, (1), pp. 87–95.
    20. 20)
      • 16. Tunaboylu, N.S., Collins, E.R., Chaney, P.R.: ‘Voltage disturbance evaluation using the missing voltage technique’. Proc. Eighth Int. Conf. on Harmonics and Quality of Power Proc., 1998, October 1998, vol. 1, pp. 577–582.
    21. 21)
      • 15. Bollen, M.H.J.: ‘Algorithms for characterizing measured three-phase unbalanced voltage dips’, IEEE Trans. Power Deliv., 2003, 18, (3), pp. 937–944.
    22. 22)
      • 4. Wang, J., Chen, S., Lie, T.T.: ‘A systematic approach for evaluating economic impact of voltage dips’, Electr. Power Syst. Res. , 2006, 77, pp. 145–154.
    23. 23)
      • 5. Andersson, T., Nilsson, D.: ‘Test and evaluation of voltage dip immunity’. Master's thesis, Department of Electric Power Engineering, Chalmers University of Technology, Gothenburg (Sweden), 2002.
    24. 24)
      • 13. Honrubia, -Escribano, A., García-Sánchez, T., Gómez-Lázaro, E., et al: ‘Power quality surveys of photovoltaic power plants: characterization and analysis of grid-code requirements’, IET Renew. Power Gener., 2015, 9, (5), pp. 466–473.
    25. 25)
      • 3. Bollen, M.H.J., Olguin, G., Martins, M.: ‘Voltage dips at the terminals of wind power installations’, Wind Energy, 2005, 8, pp. 307–318.
    26. 26)
      • 21. Gómez-Lázaro, E., Fuentes, J.A., Molina-Garcia, A., et al: ‘Characterization and visualization of voltage dips in wind power installations’, IEEE Trans. Power Deliv., 2009, 24, (4), pp. 2071–2078.
    27. 27)
      • 14. Zhan, L., Bollen, M.H.: ‘Characteristic of voltage dips (sags) in power systems’, IEEE Trans. Power Deliv., 2000, 15, (2), pp. 827–832.
    28. 28)
      • 31. Garcia-Sánchez, T, Gómez-Lázaro, E., Muljadi, E., et al: ‘Statistical and clustering analysis for disturbances: a case study of voltage dips in wind farms’, IEEE Trans. Power Deliv., 2016, 31, (6), pp. 2530–2537.
    29. 29)
      • 26. Bollen, M.H.J., Gu, I.Y.H., Santoso, S., et al: ‘Bridging the gap between signal and power’, IEEE Signal Process. Mag., 2009, 26, (4), pp. 12–31.
    30. 30)
      • 6. IEEE Recommended Practice for Evaluating Electric Power System Compatibility With Electronic Process Equipment, " in IEEE Std 1346-1998, vol., no., pp.0_1-, 1998 doi: 10.1109/IEEESTD.1998.87816.
    31. 31)
      • 11. Leborgne, R., Olguin, G., Bollen, M.H.J.: ‘The influence of pq-monitor connection on voltage dip measurements’. IEE MedPower Conf., Lemesos, vol. 1, November 2004.
    32. 32)
      • 8. Saini, M.K., Kapoor, R.: ‘Classification of power quality events – a review’, Int. J. Electr. Power Energy Syst., 2012, 43, (1), pp. 11–19. Available at http://www.sciencedirect.com/science/article/pii/S014206151200172X.
    33. 33)
      • 30. Hastie, T., Tibshirani, R., Friedman, J.: ‘The elements of statistical learning: data mining, inference and prediction’ (USA, Springer, 2009, 2nd edn.).
    34. 34)
      • 22. Ignatova, V., Granjon, P., Bacha, S.: ‘Space vector method for voltage dips and swells analysis’, IEEE Trans. Power Deliv., 2009, 24, (4), pp. 2054–2061.
    35. 35)
      • 28. Halíř, R., Flusser, J.: ‘Numerically stable direct least squares fitting of ellipses’. Proc. Sixth Int. Conf. in Central Europe on Computer Graphics and Visualization. WSCG ’98, Plzeň, Czech Republic: CZ, February 1998, pp. 125–132.
    36. 36)
      • 32. Mora-Flórez, J., Cormane-Angarita, J., Ordóñez-Plata, G.: ‘k-means algorithm and mixture distributions for locating faults in power systems’, Electr. Power Syst. Res., 2009, 79, (5), pp. 714–721. Available at http://www.sciencedirect.com/science/article/pii/S0378779608002800.
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