Grounding System Cost Analysis Using Optimization Algorithms

• Published in: Energies (Basel) 2018-12, Vol.11 (12), p.3484
• Main Authors: Perng, Jau-Woei, Kuo, Yi-Chang, Lu, Shih-Pin
• Format: Article
• Language: English
• Subjects: Algorithms; artificial bee colony; Cardiac arrhythmia; Chromosomes; Copper; Costs; Design; Electric currents; Energy; Engineering; ETAP; Fibrillation; fuzzy algorithm; Fuzzy sets; genetic algorithm; Genetic algorithms; Human body; IEEE Std. 80-2000; International conferences; multi-objective particle swarm optimization algorithm; Nervous system; neural network; Optimization; Optimization algorithms; Radial basis function; Rods; Schwarz’s equation; Skin; Systems design; Ventricle; Ventricular fibrillation
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11123484

In this study, the concept of grounding systems is related to the voltage tolerance of the human body (human body voltage tolerance safety value). The maximum touch voltage target and grounding resistance values are calculated in order to compute the grounding resistance on the basis of system data. Typically, the grounding resistance value is inversely proportional to the laying depth of the grounding grid and the number of grounded copper rods. In other words, to improve the performance of the grounding system, either the layering depth of the grounding grid or the number of grounded copper rods should be increased, or both of them should be simultaneously increased. Better grounding resistance values result in increased engineering costs. There are numerous solutions for the grounding target value. Grounding systems are designed to find the combination of the layering depth of the grounding grid and the number of grounded copper rods by considering both cost and performance. In this study, we used a fuzzy algorithm on the genetic algorithm (GA), multi-objective particle swarm optimization (MOPSO) algorithm, Bees, IEEE Std. 80-2000, and Schwarz’s equation based on a power company’s substation grounding system data to optimize the grounding resistance performance and reduce system costs. The MOPSO algorithm returned optimal results. The radial basis function (RBF) neural network curve is obtained by the MOPSO algorithm with three variables (i.e., number of grounded copper rods, grounding resistance value, and grounding grid laying depth), and the simulation results of the electrical transient analysis program (ETAP) system are verified. This could be a future reference for substation designers and architects.