Numerical Investigation of the Use of Electrically Conductive Concrete-Encased Electrodes as Potential Replacement for Substation Grounding Systems
Bibliographic record
Abstract
This paper presents a numerical investigation regarding the possibility of using electrically conductive concrete (ECON) combined with concrete-encased electrode (CEE) technology to develop new substation grounding systems (SGSs) called ECON-EE as a replacement for conventional copper or galvanized steel grounding grids. In the first step, the validation of the commercial FEM software used to perform grounding system analysis was performed in terms of the grid resistance (RG), ground potential rise (GPR), and step and touch voltages, using a symmetrical 70 m × 70 m conventional copper SGS. Next, several numerical simulations of an ECON-EE grounding system with the same dimensions as the conventional copper grid used for FEM software validation were performed. Thus, several parameters of the ECON-EE grounding system were studied, such as the geometry, dimensions, and resistivity of ECON and the diameter of the rebar. The numerical results obtained permit us to demonstrate that ECON-EE grounding systems can perform better than conventional SGSs equipped with vertical rods, particularly in the case of high ground resistivity. Moreover, it was demonstrated that the two main ECON-EE parameters affecting the grounding resistance and the touch and step voltages are the section area and the resistivity of the ECON. As discussed in detail in this paper, the proposed ECON-EE grounding system can offer several advantages compared to conventional SGSs in terms of efficiency and durability, as well as in terms of simplicity of conception and implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".