Framework for optimal grounding system design concerning IEEE standard

  • Sherif S. M. GhoneimEmail author
  • Ayman Hoballah
  • Nehmdoh A. Sabiha
Original Paper


This paper investigates the optimal grounding grid design using artificial intelligence techniques based on the empirical formula for grounding resistance (Rg), touch and step voltages (Et and Es), which are addressed in IEEE Std. 80-2013/Cor. 1-2015. The objective function is formulated based on the grid conductors’ material cost and the installation cost. Particle swarm optimization (PSO) and genetic algorithm (GA) are individually utilized to search for and confirm the global best solution of minimizing the grounding system cost with considering all operation constraints including the safety criteria. A modified objective function is constructed based on self-adaptive penalization utilized to account for constraint violations during the optimization process. The selection strategy for modifying the local best positions of particles and the global best position of the swarm is proposed to enhance the ability of PSO for fast convergence. Results proved that either PSO or GA settles on the global best solution, successfully. To perform the space domain investigation, the optimal grounding grid design is implemented using a finite element method, where the COMSOL Multiphysics program is selected to verify the settled optimal global solution. Using COMSOL, the grounding resistance and safety criteria are evaluated over the grid diagonally. The COMSOL results ensure the operating constraints satisfaction of the optimal grounding grid design. The performance evaluation reveals that the grounding potential rise limit, the available grid area, and the number of vertical rods greatly affect the optimization of the grounding system design. The results also illustrate a great significant effect of the fault current and upper surface resistivity on the optimal grid design.


Grounding systems Particle swarm optimization Genetic algorithm Earth surface potential Grounding resistance 



  1. 1.
    Nedi F (2004) A new evolutionary method for designing grounding grids by touch voltage control. In: 2004 IEEE international symposium on industrial electronics, Ajaccio, France, 4–7 May 2004, vol 2, pp 1501–1505Google Scholar
  2. 2.
    Kara S, Kalenderli O, Altay O (2015) Optimum grounding grid design by using genetic algorithms. In: 9th international conference on electrical and electronics engineering (ELECO), Bursa, Turkey, 26–28 November 2015, pp 1117–1121Google Scholar
  3. 3.
    IEEE Guide for Safety in AC Substation Grounding. In IEEE Std 80-2013 (Revision of IEEE Std 80-2000/Incorporates IEEE Std 80-2013/Cor 1-2015), pp. 1–226, 15 May 2015Google Scholar
  4. 4.
    Lee CY, Shen YX (2009) Optimal planning of ground grid based on particle swam algorithm. World Acad Sci Eng Technol 60:30–37Google Scholar
  5. 5.
    Otero AF, Cidrás J, Garrido C (2002) Grounding grid design using evolutionary computation-based methods. Electr Power Compon Syst 30(2):151–165CrossRefGoogle Scholar
  6. 6.
    Khodr HM (2009) Optimal methodology for the grounding systems design in transmission line using mixed-integer linear programming. Electr Power Compon Syst 38(2):115–136CrossRefGoogle Scholar
  7. 7.
    Naveen S, Kumar KS, Rajalakshmi K (2015) Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm. Int J Electr Power Energy Syst 69:90–97CrossRefGoogle Scholar
  8. 8.
    Pegado R, Naupari Z et al (2019) Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO. Electr Power Syst Res 169:206–213CrossRefGoogle Scholar
  9. 9.
    Gerez C, Silva LI, Belati EA et al (2019) Distribution network reconfiguration using selective firefly algorithm and a load flow analysis criterion for reducing the search space. IEEE Access 7:67874–67888CrossRefGoogle Scholar
  10. 10.
    Xing H, Sun X (2017) Distributed generation locating and sizing in active distribution network considering network reconfiguration. IEEE Access 5:14768–14774CrossRefGoogle Scholar
  11. 11.
    Chen G, Qian J, Zhang Z, Sun Z (2019) Applications of novel hybrid bat algorithm with constrained pareto fuzzy dominant rule on multi-objective optimal power flow problems. IEEE Access 7:52060–52084CrossRefGoogle Scholar
  12. 12.
    Jia YH, Chen WN, Gu T, Zhang H, Yuan H, Lin Y, Yu WJ, Zhang J (2018) A dynamic logistic dispatching system with set-based particle swarm optimization. IEEE Trans Syst Man Cybern Syst 48(9):1607–1621CrossRefGoogle Scholar
  13. 13.
    Dustegor D, Poroseva SV, Hussaini MY, Woodruff S (2010) Automated graph-based methodology for fault detection and location in power systems. IEEE Trans Power Delivery 25(2):638–646CrossRefGoogle Scholar
  14. 14.
    Alik B, Teguar M, Mekhaldi A (2015) Minimization of grounding system cost using PSO, GAO and HPSGAO techniques. IEEE Trans Power Delivery 30(6):2561–2569CrossRefGoogle Scholar
  15. 15.
    Ghoneim SSM, Taha IBM (2016) Control the cost, touch and step voltages of the grounding grids design. IET Sci Meas Technol 10:943–951CrossRefGoogle Scholar
  16. 16.
    Elfergani A (2013) Accelerated particle swarm optimization-based approach to the optimal design of substation grounding grid. Przegląd Elektrotech 89:30–31Google Scholar
  17. 17.
    Opara K, Arabas J (2012) Decomposition and metaoptimization of mutation operator in differential evolution. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM (eds) Swarm and evolutionary computation. Lecture notes in computer science, vol 7269. Springer, Berlin, pp 110–118. CrossRefGoogle Scholar
  18. 18.
    Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18. CrossRefGoogle Scholar
  19. 19.
    Gilbert G, Chow YL, Bouchard DE, Salama MMA (2011) Optimization of high voltage substations using a random walk technique. In: 2011 IEEE PES 12th international conference on transmission and distribution construction, operation and live-line maintenance (ESMO 2011), Providence, Rhode Island, USA, 16–19 May 2011, pp 1–7Google Scholar
  20. 20.
    Tessema B, Yen GG (2006) A self adaptive penalty function based algorithm for constrained optimization. In: 2006 IEEE congress on evolutionary computation, CEC 2006,16–21 July 2006, Vancouver, BC, Canada, pp 246–253Google Scholar
  21. 21.
    Hoballah A, Erlich I (2009) PSO-ANN approach for transient stability constrained economic power generation. In: PowerTech, 2009 IEEE Bucharest, Bucharest, Romania, 28 June–2 July 2009, pp 1–6Google Scholar
  22. 22.
    Hoballah A, Erlich I (2012) Online market-based rescheduling strategy to enhance power system stability. IET Gener Transm Distrib 6(1):30–38CrossRefGoogle Scholar
  23. 23.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, WA, vol 4, pp 1942–1948Google Scholar
  24. 24.
    Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  25. 25.
    Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proc IEEE Congr Evolut Comput 3:1951–1957Google Scholar
  26. 26.
    AC/DC Module user’s guide, 1998–2015 COMSOLGoogle Scholar
  27. 27.
    Sverak JG (1981) Sizing ground conductors against fusing. IEEE Trans Power Appar Syst PAS-100(1):51–59CrossRefGoogle Scholar
  28. 28.
    Sullivan JA (1998) Alternative earthing calculations for grids and rods. IEE Proc Transm Distrib 145(3):271–280CrossRefGoogle Scholar
  29. 29.
    Sverak JG (1984) Simplified analysis of electrical gradients above a ground grid, part I, how good is the present IEEE method? (A special report forWG 78.1). IEEE Trans Power Appar Syst PAS-103(1):7–25CrossRefGoogle Scholar
  30. 30.
    Thapar B, Gerez V, Balakrishnan A, Blank DA (1991) Simplified equations for mesh and step voltages in an AC substation. IEEE Trans Power Delivery 6(2):601–607CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical Engineering Department, College of EngineeringTaif UniversityTaifKingdom of Saudi Arabia
  2. 2.Electrical Engineering Department, Faculty of Industrial EducationSuez UniversitySuezEgypt
  3. 3.Department of Electrical Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  4. 4.Electrical Engineering Department, Faculty of EngineeringMenofia UniversityShebin ElkomEgypt

Personalised recommendations