LAUSR

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.