For the design of reliable power grounding systems for critical electrical infrastructure, soil resistivity investigation is crucial. Nevertheless, soil resistivity will depend on various geotechnical properties, necessitating the necessity for robust assessment methods. In a brand new study, researchers conducted a comprehensive investigation into the behavior and relationships between soil resistivity and key geotechnical parameters and developed a predictive model based on their findings. This model can result in cost-effective and more reliable design of grounding systems.
Proper power grounding systems are mandatory for maintaining the security and reliability of critical electrical subsystem infrastructure, comparable to substations. Power grounding systems provide a low-resistance path for electrical fault currents to flow into the earth, stopping electrical shocks, fires, and damage to vital equipment. Investigation of soil resistivity is crucial for designing power grounding systems. For essentially the most cost-effective and efficient grounding systems for electrical substations, it’s imperative to rigorously select sites with the bottom soil electrical resistivity. This also ensures optimal performance and safety. Subsequently, accurate determination of soil resistivity is significant, as inaccurate values can result in faulty grounding systems.
The Electricity Generating Authority of Thailand has set criteria for the soil resistivity required for substations, with a threshold of lower than 80 Ohm-meters. Nevertheless, soil resistivity often fails to fulfill these requirements, highlighting the necessity for robust soil resistivity assessment methods. Many studies have investigated the connection between soil resistivity and various geotechnical properties, emphasizing the influence of water content, unit weight of soil, salt content, clay content, and particle sizes. Despite these insights, there stays a necessity for a comprehensive predictive model that integrates the relationships between soil electrical resistivity and multiple geotechnical parameters.
To deal with this challenge, a research team, led by Professor Shinya Inazumi from the College of Engineering at Shibaura Institute of Technology, conducted a comprehensive investigation into the behavior and relationships between soil resistivity and geotechnical parameters in a controlled temperature and humidity environment. In addition they developed a predictive model based on their findings. Their study was made available online on August 08, 2024, and published in Volume 23 of the journal Leads to Engineering in September 2024.
“At the center of this study is the event of predictive models based on the connection between soil electrical resistivity and key geotechnical properties. By developing robust correlation models, we aim to accurately predict soil resistivity under field conditions. This has significant implications for the design of grounding systems in electrical substations, particularly in regions with diverse soil types comparable to Thailand,” says Prof. Inazumi.
Within the study, the researchers measured 30 soil samples from various representative locations inside the power grid substation in Thailand, using a controlled laboratory environment to ascertain a strong correlation of resistivity with each geotechnical parameter. Three index geotechnical properties were chosen to correlate with soil electrical resistivity: water content, known to strongly influence resistivity; plasticity index, representing the clay content; and dry density, representing soil density without water. Results revealed a transparent relationship between soil resistivity and water content, with resistivity increasing with decreasing water content. Nevertheless, the correlation between resistivity and plasticity index or dry density was found to be less important, which the researchers attributed to the dominant influence of water content.
To deal with this issue, they further utilized nonlinear multiple regression evaluation to check the combined effects of water content and other soil parameters. The coefficient of determination (r2), which explains how well a model suits the observed data, for the correlation of soil electrical resistivity, water content, and plasticity index, was found to be 0.8281, and 0.7742 for the correlation between electrical resistivity, water content, and dry density. These strong correlations suggest that a mixture of water content, plasticity index, and dry density provides a reliable predictive model for soil resistivity.
Nevertheless, the team also acknowledged the limitation of this model, which may currently predict soil resistivity of only cohesive soils with tremendous particles. That is resulting from the limited number of soil samples utilized in the study. Fortunately, this limitation may be easily addressed in future research by including a broader and more diverse set of soil samples.
“This study provides a technique for optimizing substation grounding designs, that are critical for shielding equipment and personnel from electrical faults. The outcomes can reduce the necessity for extensive soil testing and modifications, cutting costs while maintaining regulatory compliance. Beyond electrical applications, predictive models developed within the study may be adapted for environmental monitoring,” remarks Prof. Inazumi, highlighting the potential broader applications of their study.
Overall, this study breaks recent ground in soil resistivity assessment, contributing to the cost-effective construction of ground systems for electrical substations and paving the way in which for safer and more reliable power supply, which is crucial for stable economic growth.
