Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free High voltage underground cable bonding optimisation to prevent cable termination faults in mixed high-voltage lines

© The Institution of Engineering and Technology
Received 16/10/2019, Accepted 26/05/2020, Revised 16/05/2020, Published 02/06/2020

Cable termination fault (CTF) generally occurs due to increasing of electrical force (EF) and thermal effect (TE) in mixed line. EF increases due to sheath voltage (SV), and TE increases due to harmonic distortion (HD) on cable terminations (CTs). Also, SV and HD increase due to zero-sequence current (ZC). In the literature, different bonding methods are used to prevent CTF, but these methods are not sufficient to prevent CTF that is based on ZC. In this study, the modified sectional solid bonding (MSSB) is suggested to prevent CTF. The aims of MSSB method are to restrict EF and TE on CT. Thus, MSSB parameters are optimised to restrict SV and HD, but SV and HD on CT should be known for optimisation of MSSB parameters. Thus, SV and HD are forecasted by using adaptive neuro-fuzzy inference system (Anfis), artificial neural networks (ANNs) and hybrid ANN. Training and forecasting errors of hybrid ANN are less than Anfis and the other ANN methods. Thus, hybrid ANN methods are used as fitness function of optimisation algorithms in MSSB optimisation. When the optimised MSSB method is used for cable bonding, HD and SV on CT are restricted the determined limits to prevent CTF in long mixed line.

Inspec keywords: power cables; fuzzy neural nets; fault location; power distribution faults; optimisation; power engineering computing; underground cables; fuzzy reasoning; harmonic distortion

Other keywords: high voltage underground cable bonding optimisation; CTF; cable termination fault; different bonding methods; long mixed line; ZC; CT; optimisation algorithms; MSSB optimisation; hybrid ANN methods; EF increases; cable terminations; modified sectional solid bonding; sheath voltage; thermal effect; zero-sequence current; HD increase; optimised MSSB method; high-voltage lines; MSSB parameters

Subjects: Knowledge engineering techniques; Power engineering computing; Power system protection; Distribution networks; Power cables; Optimisation techniques; Optimisation techniques; Power system measurement and metering; Neural computing techniques

References

    1. 1)
      • 22. Gatta, F.M., Lauria, S.: ‘Shunt compensation of EHV cables and mixed overhead-cable lines’. Proc. IEEE Lausanne Power Tech., Lausanne, Switzerland, June 2007, pp. 1–6.
    2. 2)
    3. 3)
      • 4. Jittiphong, K., Atthapol, N.: ‘Fault classification on the hybrid transmission line system between overhead line and underground cable’. 17th World Congress of Int. Fuzzy Systems Association and 9th Int. Conf. on Soft Computing and Intelligent Systems (IFSA-SCIS), Otsu, Japan, June 2017, pp. 1–6.
    4. 4)
      • 12. Czapp, S., Dobrzynski, K., Klucznik, J., et al: ‘Calculation of induced sheath voltages in power cables – single circuit system versus double circuit system’, J. Inf., Control Manage. Syst., 2014, 12, pp. 113–123.
    5. 5)
      • 25. Akbal, B.: ‘OSSB and hybrid methods to prevent cable faults for harmonic containing networks’, Elektron. Elektrotech., 2018, 29, pp. 97–105.
    6. 6)
      • 17. Wei, F., Xiao, K., Qiwei, Z., et al: ‘Impact of neutral current on concentric cable overloading’. Proc. 2018 IEEE/PES Transmission and Distribution Conf. and Exposition (T&D), Denver, CO, USA, August, 2018, pp. 1–5.
    7. 7)
      • 7. Bessissa, L., Boukezzi, L., Mahi, D.: ‘Influence of fuzzy parameters on the modeling quality of XLPE insulation properties under thermal aging’, Fuzzy Inf. Eng., 2016, 8, (1), pp. 101–112.
    8. 8)
      • 31. Akbal, B.: ‘Neural-network-based current forecasting on high-voltage underground cables’. Electron. World, 2016, 22, (1959), pp. 30–34.
    9. 9)
      • 16. Glover, J.D., Sarma, M.S., Overbye, T.J.: ‘Power system analysis and design’ (Cengage Leraning, Kentucky, 2012, 5th edn.), pp. 428–431.
    10. 10)
      • 32. Benato, R.: ‘Multiconductor analysis of underground power transmission systems: EHV AC cables’. Electr. Power Syst. Res., 2009, 79, (19), pp. 27–38.
    11. 11)
      • 10. Ruiz, J.R., Garcia, A., Morera, X., et al: ‘Circulating sheath currents in flat formation underground power lines’. Proc. 2007 Int. Conf. Renewable Energies and Power Quality, Sevilla, Spain, March 2007, pp. 1–5.
    12. 12)
      • 8. Bak, C.L., Silva, F.F.: ‘High voltage AC underground cable systems for power transmission – a review of the Danish experience, part 1’, Electr. Power Syst. Res., 2016, 140, pp. 984–994.
    13. 13)
      • 20. Mehdi, N., Gerry, M.: ‘Three-phase multi module VSIs using SHE-PWM to reduce zero-sequence circulating current’, IEEE Trans. Ind. Electron., 2014, 61, (4), pp. 1659–1668.
    14. 14)
      • 18. Zhangping, S., Xing, Z., Fusheng, W., et al: ‘Modeling and elimination of zero-sequence circulating currents in parallel three-level T-type grid-connected inverters’, IEEE Trans. Power Electron., 2015, 30, (2), pp. 1050–1063.
    15. 15)
      • 14. Gouramanis, K.V., Kaloudas, C.G., Papadopoulos, T.A., et al: ‘Sheath voltage calculations in long medium voltage power cables’. Proc. IEEE Trondheim Power Tech, Norway, June 2011, pp. 1–7.
    16. 16)
      • 1. Herra, F.M.: ‘Mixed line protection’ (T&D World, Kansas, 2015), pp. 1–10.
    17. 17)
      • 34. Arı, M.: ‘154/380 kV enerji İletim Hatları Proje Uygulamaları’. Karaca Tanıtım Hizmetleri Matbaacılık Kağ. Tic. Ltd. Şti., Ankara, Turkey, 2012, p. 119.
    18. 18)
      • 21. Gatta, F.M., Lauria, S., Luigi, C.: ‘Very long EHV cables and mixed overhead-cable lines. Steady-state operation’. Proc. IEEE Russia Power Tech., St. Petersburg, Russia, June 2005, pp. 1–7.
    19. 19)
      • 2. Nanayakkara Kasun, O.M.K., Athula, D.R., Randy, W.: ‘Location of DC line faults in conventional HVDC systems with segments of cables and overhead lines using terminal measurements’, IEEE Trans. Power Deliv., 2012, 27, (1), pp. 279–288.
    20. 20)
      • 3. Shuai, Z., Houlei, G., Yingtao, S.: ‘A new fault-location algorithm for extra-high-voltage mixed lines based on phase characteristics of the hyperbolic tangent function’, IEEE Trans. Power Deliv., 2016, 31, (3), pp. 1203–1212.
    21. 21)
      • 5. Jiali, D., Xin, W., Yihui, Z., et al: ‘A novel fault location algorithm for mixed overhead-cable transmission system using unsynchronized current data’, IEEJ Trans. Electr. Electron. Eng., 2019, 14, pp. 1295–1303.
    22. 22)
      • 28. Charytoniuk, W., Chen, M.S.: ‘Very short-term load forecasting using artificial neural networks’, IEEE Trans. Power Syst., 2000, 15, pp. 263–268.
    23. 23)
      • 19. Jiangchao, Q., Maryam, S.: ‘A zero-sequence voltage injection-based control strategy for a parallel hybrid modular multilevel HVDC converter system’, IEEE Trans. Power Deliv., 2015, 30, (2), pp. 728–736.
    24. 24)
      • 30. Moutassem, W., Anders, G.J.: ‘Calculation of the eddy current and hysteresis losses in sheathed cables inside a steel pipe’, IEEE Trans. Power Deliv., 2010, 25, pp. 2054–2063.
    25. 25)
      • 9. Bak, C.L., Silva, F.F.: ‘High voltage AC underground cable systems for power transmission – a review of the Danish experience, part 2’, Electr. Power Syst. Res., 2016, 140, pp. 995–1004.
    26. 26)
      • 13. Jung, C.K., Lee, J.B., Kang, J.W.: ‘Sheath circulating current analysis of a cross-bonded power cable systems’, J. Electr. Eng. Technol., 2007, 2, pp. 320–328.
    27. 27)
      • 24. Akbal, B.: ‘Applications of artificial intelligence and hybrid neural network methods with new bonding method to prevent electroshock risk and insulation faults in high-voltage underground cable lines’, Neural Comput. Appl., 2018, 24, (2), pp. 32–36.
    28. 28)
      • 27. Weigerta, T., Tianb, Q., Lianb, Q.: ‘State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks’, J. Power Sources, 2010, 196, pp. 4061–4066.
    29. 29)
      • 15. Jung, C.K., Lee, J.B., Kang, J.W., et al: ‘Sheath current characteristic and its reduction on underground power cable systems’. Proc. IEEE Power Engineering Society General Meeting, CA, USA, August 2005, pp. 2562–2569.
    30. 30)
      • 26. Achanta, R.: ‘Long term electric load forecasting using neural networks and support vector machines’. Int. J. Comput. Sci. Technol., 2012, 3, pp. 266–269.
    31. 31)
      • 23. Akbal, B.: ‘Sectional solid bonding for grounding of high voltage underground cables to reduce the sheath current effects’, Int. J. Innovative Res. Eng. Manage., 2016, 3, (2), pp. 103–107.
    32. 32)
      • 29. Çevik, H.H., Çunkaş, M.: ‘Short-term load forecasting using fuzzy logic and Afis’, Neural Comput. Appl., 2015, 26, (6), pp. 1355–1367.
    33. 33)
      • 6. Yunus, B.: ‘Trend adjusted lifetime monitoring of underground power cable’, Electr. Power Syst. Res., 2016, 143, pp. 189–196.
    34. 34)
      • 11. Zhonglei, L., Du, B.X., Wang, L., et al: ‘The calculation of circulating current for the single-core cables in smart grid’. Proc. 2012 IEEE Innovative Smart Grid Technologies – Asia, China, May 2012, pp. 1–4.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2019.1559
Loading

Related content

content/journals/10.1049/iet-gtd.2019.1559
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address