Grounding System Cost Analysis Using Optimization Algorithms

Abstract

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.

Monitoring of MV Cable Screens, Cable Joints and Earthing Systems Using Cable Screen Current Measurements

“…Earthing systems have been researched since the beginning of power system development up to this day. Research about earthing systems is focused on lattice earth electrodes of high (> 110 kV) voltage substations [2][3][4][5], optimization of earthing systems construction and monitoring of earthing systems in depths of the network and inside substations [6][7][8][9]. Earthing systems ensure the safety of power system operation, i.e., directing lightning strikes to the ground, ensuring the safety of humans under fault conditions, and allowing for voltage measurement [10,11].…”

Grounding system cost reduction using multi-objective optimisation method

“…Perng et al . in [16] used MOPSO algorithm to optimise grounding grid with other algorithms GA and Bees and by establishing a fuzzy integral table to count the score of the optimisation algorithms to evaluate then they identified a suitable algorithm for optimising the grounding system and they concluded that MOPSO algorithm returned optimal results then radial basis function neural network have been used with MOPSO for predicting linear grounding resistance value and grounding system cost.…”

“…In [16], the authors used three variables, number of grounded copper rods, grounding resistance value and grounding grid laying depth to find optimal grounding resistance and grounding system cost. Our paper generalises this method by using eight variables that have a direct impact on design of the grounding grid and four cost functions as explained in detail in Section 2.…”

On Minimisation of Earthing System Touch Voltages

“…Other works are aimed on the optimization by minimising the total length of used ES horizontal and vertical conductors [5,6]. Some other researchers also included into the optimization the costs that are connected with the installation process, i.e., the excavation costs as well as the ES conductor material costs [5,7,8]. Another, quite a different approach to ES design optimisation can be found in [9,10].…”

“…The usage of some different objective functions has been described in the previous paragraph. However, in the case of the step direction function, the aforementioned papers [4][5][6][7][8][9][10][11] mainly 3 of 15 use two different approaches or their combination. The first approach is by performing parametric analysis (i.e., sensitivity analysis).…”