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A Framework for Wide-area Monitoring of Tree-related High Impedance Faults in Medium-voltage Networks

  • Bahador, Nooshin (Dept. of Electrical Engineering, Lorestan University) ;
  • Matinfar, Hamid Reza (Dept. of Remote Sensing, GIS and Soil Science, Lorestan Univerity) ;
  • Namdari, Farhad (Dept. of Electrical Engineering, Lorestan University)
  • Received : 2017.02.18
  • Accepted : 2017.07.18
  • Published : 2018.01.01

Abstract

Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.

Keywords

Wide-area monitoring;Tree-related high impedance fault;Off-line mapping;Spectral data

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Fig. 1. Power outages causes in the United States [4]

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Fig. 2. Current RMS of a THIF

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Fig. 3. Study area

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Fig. 4. Obtained white points representing trees’ crown inFig. 3

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Fig. 5. Location of sampling points

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Fig. 6. Electrical conductivity measurement of samples

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Fig. 7. EC values of 25 live poplar trees

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Fig. 8. Linear relationship between electrical conductivityand salt content of an electrolyte

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Fig. 9. False color composite (432) of study area

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Fig. 10. The reflectance data corresponding to each spectralband

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Fig. 11. The model fitted for electrical conductivity withthe R2 = 0.4409

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Fig. 12. Fundamental component and high frequencycomponents of tested trees

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Fig. 13. Distribution of intrinsic mode functions

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Fig. 14. Algorithm of Quantiles calculation

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Fig. 15. The quantile-quantile plots and estimated lines

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Fig. 16. HIF experiment under 20 kV power lines

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Fig. 17. Downloaded stored THIF current (mA)

Table. 1. Coefficients of regression equation

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Table 2. Max amplitude of THIF current (A)

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Table 3. Max amplitude of THIF current

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Table 4. The estimated maximum amplitude for high frequency components

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References

  1. M. Adamiak, C. Wester, M. Thakur, C. Jensen, "High impedance fault detectionon distribution feeders," GE Industrial Solution, pp. 25-31, 2006.
  2. A. Milioudis, G. Andreou, and D. Labridis, "Enhanced protection scheme for smart grids using power line communications techniques -Part I: Detection of high impedance fault occurrence," IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1621-1630, 2012. https://doi.org/10.1109/TSG.2012.2208987
  3. Apostolos N. Milioudis, Georgios T. Andreou, Dimitris P. Labridis, "Detection and Location of High Impedance Faults in Multiconductor Overhead Distribution Lines Using Power Line Communication Devices," IEEE Trans. Smart Grid, vol. 6, pp. 894- 902, 2015. https://doi.org/10.1109/TSG.2014.2365855
  4. T. Marxsen. "Power line bushfire safety program," Department of Economic Development, Jobs, Transport and Resources, Jul, 2015.
  5. J. R. Macedo, J. W. Resende, C. A. Bissochi, D. Carvalho, F.C. Castro, "Proposition of an inter harmonic-based methodology for high-impedance fault detection in distribution systems," IET Gener. Transm. Distrib. vol. 9, no. 16, pp. 2593-2601, 2015. https://doi.org/10.1049/iet-gtd.2015.0407
  6. L.U. Iurinic, A.R. Herrera-orozco, R.G. Ferraz, A.S. Bretas, "Distribution Systems High-Impedance Fault Location: A Parameter Estimation Approach," IEEE Trans. Power Deliv., vol. 31, pp. 1806-1814, 2016. https://doi.org/10.1109/TPWRD.2015.2507541
  7. N. Elkalashy, M. Lehtonen, H. Darwish, M. Izzularab, A.-M. Taalab, "Modeling and experimental verifycation of high impedance arcing fault in medium voltage networks," IEEE Trans. Dielectr. Electr. Insul. vol. 14, no. 2, pp. 375-383, 2007. https://doi.org/10.1109/TDEI.2007.344617
  8. C. H. Kim, H. Kim, Y. Ko, S. H. Byun, R. K. Aggarwal and A. T. Johns, "A Novel Fault-Detection Technique of High-Impedance Arcing Faults in Transmission Lines Using the Wavelet Transform," IEEE Trans. Power Deliv., vol. 17, no. 4, pp. 921-929, 2002. https://doi.org/10.1109/TPWRD.2002.803780
  9. R. Paththamperuma, I. Perera, K. Perera, C. Perera, N. De Silva, U. Javatunga, "High Impedance Arcing Fault Detection in Low Voltage Distribution Network," Digital Library of University of Moratuwa, pp. 1-6, 2013.
  10. V.T.H.F.R.S. Maximov, J.L. Guardado, "High impedance fault locationformulation: a least square estimator based approach," Math. Problems Eng., p. 1-10, 2014.
  11. M. G. Ahsaee, "Accurate NHIF locator utilizing twoend unsynchronized measurements," IEEE Trans. Power Del., vol. 28, no. 1, pp. 419-426, 2013. https://doi.org/10.1109/TPWRD.2012.2215889
  12. W. Costa dos Santos, B. Alencar de Souza, N. Silva Dantas Brito, F. Bezerra Costa, M. Renato Cerqueira PaesJr "High Impedance Faults: From Field Tests to Modeling", in Journal of. Control, Automation and Electrical Systems, vol. 24, no. 6, pp. 885-896, 2013. https://doi.org/10.1007/s40313-013-0072-8
  13. A. Mahari, H. Seyedi, "High impedance fault protection in transmission lines using a WPT-based algorithm," Int. J. Electr. Power Energy Syst. vol. 67, pp. 537-545, 2015. https://doi.org/10.1016/j.ijepes.2014.12.022
  14. P. Biradar, V.R. Sheelvant, "High-impedance fault detection using wavelet transform," Int. J. Eng. Res. Gen. Sci., pp. 166-173, 2015.
  15. N.R. Varma, D.B.V.S. Ram, D.K.S.R. Anjaneyulu, "Development of fault detection algorithm for high impedance faults in distribution network using multiresolution analysis," Int. J. Eng. Res. Technol. vol. 3, no. 9, pp. 573-576, 2014.
  16. I. Baqui, I. Zamora, J. Mazon, G. Buigues, "High impedance fault detection methodology using wavelet transform and artificial neural networks," Electr. Power Syst. Res. vol. 81, no. 7, pp. 1325-1333, 2011. https://doi.org/10.1016/j.epsr.2011.01.022
  17. M. A. Azpurua, M. Pous, F. Silva, "Decomposition of Electromagnetic Interferences in the Time-Domain," IEEE Trans. Electromagn. Compat., vol. 58, no. 2, pp. 385-392, 2016. https://doi.org/10.1109/TEMC.2016.2518302
  18. J. C. Chan, H. Ma, T. K. Saha, "Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding", IEEE Trans. Dielectr. Electr. Insul, vol. 21, pp. 294-303, 2014. https://doi.org/10.1109/TDEI.2013.003839
  19. A. Ghaderi, H.A. Mohammadpour, H. Ginn, High impedance fault detectionmethod efficiency: simulation vs. real-world data acquisition, in: Power and Energy Conference at Illinois (PECI), 2015 IEEE, pp. 1-5, 2015.
  20. F. Namdari, N. Bahador, "Modeling trees internal tissue for estimating electrical leakage current," IEEE Trans. Dielectr. Electr. Insul., vol. 23, pp. 1663-1674, 2016. https://doi.org/10.1109/TDEI.2016.005492
  21. F. Namdari, N. Bahador, "Modeling trees internal tissue for estimating electrical leakage current," In Progress: IET Gener. Transm. Distrib., Aug. 2017.
  22. R.K. Sairam, K.R. Veerabhadra, G.C. Srivastava. "Differential response of wheat genotypes to longterm salinity stress in relation to oxidative stress, antioxidant activity and osmolyte concentration," Plant Sci., vol. 163, pp. 1037-1046, 2002. https://doi.org/10.1016/S0168-9452(02)00278-9
  23. G.G. Aseyev. "Supramolecular Interactions and Non- Equilibrium Phenomena in concentrated solutions Georgii Georgievich Aseyev," CRC Press, 2014.
  24. N. R. Bahador, F. Namdari. And H. R. Matinfar, "Feature extraction of tree-related high impedance faults as a source of electromagnetic interference around medium voltage power lines' corridors," Progress In Electromagnetics Research B, vol. 75, pp. 13-26, 2017. https://doi.org/10.2528/PIERB17022802