Determination of Appropriate Soil Model and Parameters for Grounding System of Substation With High Voltage Via Kronecker-Sequenced Genetic Algorithms

Abstract

Accurate estimation of a suitable soil model and its parameters from field resistivity measurements, as well as the apparent soil resistivity based on this model,
is vital for the reliable design of grounding systems in substations with high voltage. In this study, the two-layer soil model parameters are estimated based on
The Institute of Electrical and Electronics Engineers (IEEE) Standards using Kronecker-sequenced genetic algorithms (GAs), with lower error values compared to
those in the literature. The estimated parameters are upper and lower layer soil resistivities and upper layer depth. Furthermore, grounding grids are individually designed for both uniform and two-layer soil models using Kronecker-sequenced GAs. The design parameters are the number of rods, meshes, and grid
burial depth. In the grounding grid designs, in addition to safety requirements, cost minimization is a key objective. Subsequently, the uniform soil model design
parameters have been applied to the two-layer soil model, and the two-layer soil model design parameters to the uniform soil model. This approach enabled
the assessment of whether the grounding grid design parameters obtained for one soil model type (uniform or two-layer soil models) could satisfy safety criteria when applied to the other. Thus, conclusions are drawn on the suitability and reliability of uniform and two-layer soil models for grounding system design. In
this study, without using very expensive commercial software, Kronecker-sequenced GAs are employed for the estimation of two-layer soil model parameters
and the apparent soil resistivity corresponding to the uniform soil model, the grounding grid design, and the evaluation of soil model suitability.

Cite this article as: B. Gürsu, “Determination of appropriate soil model and parameters for grounding system of substation with high voltage via Kroneckersequenced genetic algorithms,” Turk J Electr Power Energy Syst., Published online September 11, 2025. doi: 10.5152/tepes.2025.25026.