Skip to main content

Advertisement

SpringerLink
Log in

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

  • New Trends in data pre-processing methods for signal and image classification
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The sheath current causes cable faults and electroshocks in the high-voltage underground cable lines. Thus, the sheath current must be determined before a new high-voltage cable line is installed to take the required precautions against the sheath current effects. In this study, PSCAD/EMTDC is used for simulation of high-voltage underground cable line to examine the sheath current. The sheath current is forecasted with artificial neural network (ANN) by using the results of simulation studies, and differential evolution algorithm (DEA), genetic algorithm (GA), clonal selection algorithm (CSA), hybrid DEA-CSA, artificial bee colony (ABC), particle swarm optimization (PSO) and inertia weight PSO (iPSO) are used to find the best weights of ANN. The sheath current formation factors which are obtained in PSCAD/EMTDC are used as inputs of ANN. It is seen at the end of simulation studies that many factors affect the formation of the sheath current, so Pearson correlation is used to determine the most effective sheath current formation factors for inputs of ANN. In the literature, three types of bonding methods are used to reduce the sheath current and voltage, but these methods are not sufficient to reduce both sheath current and the sheath voltage simultaneously. Therefore, in this study, the sectional solid bonding method is developed as new bonding method to reduce the sheath voltage and current in this study. When the sectional solid bonding is used, the sheath voltage can be reduced under touch voltage, and the sheath current can be reduced under the desired value.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Jung CK, Lee JB, Kang JW (2007) Sheath circulating current analysis of a cross-bonded power cable systems. J Electron Eng Technol 2:320–328

    Article  Google Scholar 

  2. Gouda OE, Farag AA (2012) Factors affecting the sheath losses in single-core underground power cables with two-points bonding method. Int J Electron Comput Eng 7:7–16

    Google Scholar 

  3. Moutassem W, Anders GJ (2010) Calculation of the eddy current and hysteresis losses in sheathed cables inside a steel pipe. IEEE Trans Power Deliv 25:2054–2063

    Article  Google Scholar 

  4. Gordon GL, Yang CF, Huang MH, Su CT (2008) Optimal connection of power transmission lines with underground power cables to minimize magnetic flux density using genetic algorithms. IEEE Trans Power Deliv 23:1553–1560

    Article  Google Scholar 

  5. Jung CK, Lee JB, Kang JW, Wang XH, Song YH (2005) Sheath current characteristic and its reduction on underground power cable systems. In: IEEE 2005 power engineering society general meeting, San Francisco, USA, pp 2562–2569

  6. Gouramanis KV, Kaloudas CG, Papadopoulos TA, Papagiannis GK, Stasinos EK (2011) Sheath voltage calculations in long medium voltage power cables. In: IEEE 2011 Trondheim power tech Trondheim, Norway, pp 1–7

  7. Chen X, Wu K, Cheng Y, Yan L (2007) Sheath circulating current calculations and measurements of underground power cables. Jicable 1:1–4

    Google Scholar 

  8. Yaghini M, Khoshraftar M, Fallahi M (2013) A hybrid algorithm for artificial neural network training. Eng Appl Artif Int 26:293–301

    Article  Google Scholar 

  9. Demetrios AT (2006) Protection of high-voltage AC cables. In: Proceedings of power systems conference. Advanced metering, protection, control, communication, and distributed resources, College Station, TX, pp 48–61

  10. Gudmundsdottir US, Gustavsen B, Bak CL, Wiechowski W (2011) Field test and simulation of a 400-kV cross-bonded cable system. IEEE Trans Power Deliv 26:1403–1410

    Article  Google Scholar 

  11. Oshaba AS, Ali ES, Abd-Elazim SM (2015) PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor-pump load. Neural Comput Appl. doi:10.1007/s00521-015-2067-9

    Google Scholar 

  12. Akbal B, Ürkmez A (2013) The parallel resonance impedance detection method for parameter estimation of power line and transformer by using CSA, GA and PSO. Turk J Electr Eng Comput Sci 24:12–23

    Article  Google Scholar 

  13. Akbal B, Aydın M (2014) DE-Otsu method to eliminate ice load effect. J Appl Environ Biol Sci 4:423–427

    Google Scholar 

  14. Akbal B, Urkmez A (2013) PSO and inertia weight to determine parallel resonance power in electrical networks. In: 5th international conference on electronics, computers and artificial intelligence, Pitesti, Romania, pp 13–18

  15. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  16. Benato R (2009) Multiconductor analysis of underground power transmission systems: EHV AC cables. Electr Power Syst Res 79:27–38

    Article  Google Scholar 

  17. Ruiz JR, Garcia A, Morera XA (2007) Circulating sheath currents in flat formation underground power lines. In: 2007 international conference on renewable energies and power quality, Seville, Spain, pp 1–5

  18. Zhonglei L, Du BX, Wang L, Yang C, Liu HJ (2012) The calculation of circulating current for the single-core cables in smart grid. In: IEEE 2012 innovative smart grid technologies—Asia, Tianjin, China, pp 1–4

  19. Czapp S, Dobrzynski K, Klucznik J, Lubosny Z (2014) Calculation of induced sheath voltages in power cables—single circuit system versus double circuit system. J Inf Contr Manage Syst 12:113–123

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Selçuk University, Konya, Turkey

    Bahadır Akbal

Authors
  1. Bahadır Akbal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bahadır Akbal.

Ethics declarations

Conflict of interest

There is no any conflict of interest in this study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 204 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 & Applic 29, 97–105 (2018). https://doi.org/10.1007/s00521-017-2860-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2860-8

Keywords

Search

Navigation