The examination of soil resistivity is vital for creating dependable power grounding systems in essential electrical infrastructure. Soil resistivity is influenced by various geotechnical characteristics, highlighting the necessity for effective evaluation techniques. Recently, researchers undertook an in-depth study to explore the interactions and behaviors between soil resistivity and significant geotechnical factors, culminating in the development of a predictive model grounded in their analysis. This model has the potential to produce grounding systems that are both more cost-effective and trustworthy.
Effective power grounding systems are essential for preserving the safety and reliability of crucial electrical infrastructure, such as substations. These systems allow electrical fault currents to safely dissipate into the earth, averting risks of electrical shocks, fires, and harm to essential equipment. To design efficient power grounding systems, studying soil resistivity is fundamental. It is vital to select locations with the lowest soil electrical resistivity to ensure economic and effective grounding systems for electrical substations, ultimately enhancing performance and safety. Therefore, accurately assessing soil resistivity is imperative; any errors in these measurements could compromise the grounding system’s integrity.
The Electricity Generating Authority of Thailand has established soil resistivity criteria for substations, mandating a maximum of 80 Ohm-meters. Unfortunately, many sites fail to satisfy these standards, underscoring the need for reliable soil resistivity assessment techniques. Numerous studies have examined the connections between soil resistivity and various geotechnical traits, demonstrating the impact of factors such as water content, soil unit weight, salt content, clay content, and particle sizes. Despite this knowledge, there is a persistent demand for a thorough predictive model that encompasses these relationships comprehensively.
To tackle this issue, a research team, spearheaded by Professor Shinya Inazumi from the College of Engineering at Shibaura Institute of Technology, carried out an extensive study on how soil resistivity relates to geotechnical parameters, all within a controlled environment of consistent temperature and humidity. They also formulated a predictive model based on their observations. This research was published online on August 08, 2024, and appeared in Volume 23 of the journal Results in Engineering in September 2024.
“Central to this research is the creation of predictive models correlating soil electrical resistivity with essential geotechnical properties. Through the development of solid correlation models, we aim to precisely forecast soil resistivity under real-world conditions. This has considerable implications for designing grounding systems in electrical substations, especially in areas like Thailand, where soil types can vary,” stated Prof. Inazumi.
In their research, the team analyzed 30 soil samples collected from various sites within the power grid substation in Thailand. They utilized a controlled laboratory setting to establish a strong correlation of resistivity with each geotechnical property. The researchers selected three key geotechnical parameters to pair with soil electrical resistivity: water content, which significantly affects resistivity; plasticity index, indicating clay content; and dry density, reflecting the soil’s density in the absence of moisture. Their findings revealed a clear connection between soil resistivity and water content, noting that resistivity increases as water content decreases. On the other hand, the correlations with plasticity index and dry density were not as pronounced, mainly due to the overriding effect of water content.
To further investigate this matter, they applied nonlinear multiple regression analysis to understand the combined impact of water content along with other soil properties. The coefficient of determination (r2) for the correlation models involving soil electrical resistivity, water content, and plasticity index reached 0.8281; while that for resistivity, water content, and dry density was 0.7742. These strong correlations indicate that utilizing a combination of water content, plasticity index, and dry density can yield a dependable predictive model for soil resistivity.
Nevertheless, the team recognized a limitation regarding the current model’s applicability, as it can only predict soil resistivity for cohesive soils with fine particles, attributed to the limited variability of soil samples in the study. This challenge can be effectively addressed in future research by incorporating a wider range of soil samples.
“This research offers a strategy for optimizing grounding designs for substations, which play a crucial role in safeguarding both equipment and personnel from electrical hazards. The findings can reduce the need for extensive soil testing and modifications, resulting in cost savings while ensuring compliance with regulations. Moreover, the predictive models established in this study could find applications beyond electrical contexts, such as in environmental monitoring,” highlighted Prof. Inazumi, underscoring the potential for broader applications of their work.
In summary, this study represents a significant advancement in assessing soil resistivity, contributing to the cost-effective development of grounding systems for electrical substations and paving the way for safer and more reliable power supply, which is critical for stable economic development.
