Prediction of Soil Resistivity and Ground Rod Resistance for Deep Ground Electrodes

Blattner, Christian

doi:10.1109/tpas.1980.319870

Cited by 27 publications

(8 citation statements)

References 3 publications

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“…The soil resistivity values are then interpreted into a 2-layer soil model using genetic algorithm (GA), as can be found in Refs [16][17][18][19]. The details of the interpretation techniques can be found in some of the authors' publications [16][17][18][19]. The soil resistivity values are presented in Section IVA.…”

Section: A Low Magnitude Currents and At Low Frequencymentioning

confidence: 99%

“…[14] and interpreted into 2-layer soil models. The details of the soil resistivity values, and interpretation techniques using the Genetic Algorithm as in Ref [15][16][17][18][19] are shown in the Appendix. Here, the results showed that the soil resistivity values are 130.5 Ωm and 93.225 Ωm respectively for first and second layers of soil.…”

Section: A Low Magnitude Currents and At Low Frequency Earth Resistimentioning

confidence: 99%

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Performance of Earthing Systems for Different Earth Electrode Configurations

Yunus

Nor

Etobi

et al. 2015

IEEE Trans. on Ind. Applicat.

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Section: A Low Magnitude Currents and At Low Frequencymentioning

confidence: 99%

Section: A Low Magnitude Currents and At Low Frequency Earth Resistimentioning

confidence: 99%

Performance of Earthing Systems for Different Earth Electrode Configurations

Yunus

Nor

Etobi

et al. 2015

IEEE Trans. on Ind. Applicat.

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“…In case resistivity values are given, it is assumed that the values have been generated with the formulae given in Section 2.1. The initial guess at the start of the algorithm is computed with an approximate technique described in [8]. Figure 8 provides, in a tabular form, the computed error of the parameters (uncertainty) versus confidence level.…”

Section: Estimation Of Soil Parameter Errorsmentioning

confidence: 99%

“…Some of these techniques have been developed since the early days of electric power systems [13,14]. However, it is reported that many times these techniques perform poorly [2,8].…”

Section: Introductionmentioning

confidence: 99%

Interpretation of Soil Resistivity Measurements: Experience with the Model SOMIP

Meliopoulos

Papalexopoulos

1986

IEEE Trans. Power Delivery

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A method and a computer program for interpreting soil measurement data are presented.The method employs a statistical estimation of soil parameters from four pin or three pin measurements. The analysis provides (1) the best estimate of soil parameters, (2) the error of the parameters versus confidence level, (3) a pictorial view of how well the estimated soil model fits the measurements, and (4) the measurements which are not consistent (bad measurements). The methodology is applicable to data obtained with four or three pin method over a small or a large area.It performs best when applied to measurements taken at various locations of the area of interest. A computer program has been developed based on this methodology with the symbolic name SOMIP (SOil Measurements Interpretation Program). Implementation details are discussed in this paper. The performance of the methodology is compared with test model data. In addition, the experience gained in interpreting soil resistivity measurement with the program SOMIP is presented in this paper. Specifically, many sets of actual data, taken from four major utilities, has been analyzed and the results are presented and discussed.

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“…The statistical quantifications of subsisting grounding system arrangements are discussed in [4][5][6][7][8][9]. In these studies, the influence on the demeanor of grounding electrodes soil layer and its resistivity has been investigated.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2015

IJSC

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The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN) to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN) to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.

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Prediction of Soil Resistivity and Ground Rod Resistance for Deep Ground Electrodes

Cited by 27 publications

References 3 publications

Performance of Earthing Systems for Different Earth Electrode Configurations

Performance of Earthing Systems for Different Earth Electrode Configurations

Interpretation of Soil Resistivity Measurements: Experience with the Model SOMIP

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