An analysis of conventional grounding impedance based on the impulsive current distribution of a horizontal electrode

Choi, Jong-Hyuk; Lee, Bok-Hee

doi:10.1016/j.epsr.2011.07.005

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…The equivalent volume of each segment is computed by considering the calculated Figure 1. Soil structure of the test site nearby the horizontal electrode with a length of 50 m, adapted from [26]. SOIL IONIZATION EFFECT ON GROUND RESISTANCE electric field value of each segment.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…80, is only valid when the variation in the apparent resistivity is moderate [21]. For example, Figure 1 shows that the variation of the soil resistivity in the test site near the horizontal electrode with a length of 50 m is very evident [26]. However, the earth is a non-homogeneous medium, and the soil properties (ρ and ε) vary significantly, both laterally and with respect to depth [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…However, the earth is a non-homogeneous medium, and the soil properties (ρ and ε) vary significantly, both laterally and with respect to depth [23][24][25]. For example, Figure 1 shows that the variation of the soil resistivity in the test site near the horizontal electrode with a length of 50 m is very evident [26]. In non-homogeneous soil conditions, each portion of the soil in the vicinity of the grounding conductor individually affects the grounding resistance value, owing to soil ionization.…”

Section: Introductionmentioning

confidence: 99%

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The effect of soil ionization on transient grounding electrode resistance in non-homogeneous soil conditions

Mokhtari

Abdul‐Malek

Salam

2015

Int. Trans. Electr. Energ. Syst.

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Summary The soil ionization phenomenon effectively reduces the grounding electrode resistance. The existing methods to determine the soil ionization effect on the transient resistance of a grounding electrode are valid only for homogeneous soils. However, most soil structures are non‐homogeneous. Realizing the importance of the issue, this study proposes a hybrid method to incorporate the soil ionization effect on the transient resistance of a grounding electrode located in non‐homogeneous soil condition. The electromagnetic field approach is utilized to compute the ionized soil volume around the electrode to determine the equivalent radius of the electrode. A circuit‐based method of computing the grounding resistance of the electrode in non‐homogeneous soil conditions is proposed based on the per‐unit‐length distributed parameter method. The proposed method is validated by using an electromagnetic field‐based numerical solution for the non‐homogeneity effect of the soil on grounding electrode resistance. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The effect of soil ionization on transient grounding electrode resistance in non-homogeneous soil conditions

Mokhtari

Abdul‐Malek

Salam

2015

Int. Trans. Electr. Energ. Syst.

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“…It is this algorithm that has been the subject of our estimation of the soil electrical resistivity using the Gaussian kernel, widely used in practice, which is evaluated according to relation (6).…”

Section: Support Vectors Machines (Svm)mentioning

confidence: 99%

Estimation of Soil Electrical Resistivity Using Mlp, Rbf, Anfis and SVM Approaches.

Djandja¹

2019

IJAR

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The knowledge of soil electrical resistivity proves essential for a better earthing in order to ensure the protection of telecommunications and electrical energy networks. This study aims to estimate the value of the electrical resistivity of a site's soil from soil humidity and ambient temperature. The data used were measured at sites in the city of Lomé and its surroundings. We developed models using Artificial Neural Network (precisely Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF)), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). The MAPE (Mean Absolute Percentage Error) errors obtained are 0.0011761% for the MLP model, 0.0719309% for the RBF model, 0.00105% for the ANFIS model and 2.89466% for the SVM model. We can say that the results are satisfactory for all models but the ANFIS model is better, given these performances compared to other models. The latter is then retained for the prediction of soil electrical resistivity.

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“…Then, leaders give themselves as a duty to protect energy networks in order to meet the ever-increasing needs of the population. One of the quality factors of this protection is the earthing system, which plays an important role in the telecommunications and power distribution networks for the safe operation of any installation (Nassereddine et al, 2013;Choi and Lee, 2012;Kamel, 2011;Houndedako et al, 2014;Nzuru Nsekere, 2009). Thus, a properly designed earthing system is able to dissipate large currents to the earth safely, regardless of the type of fault.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soils Electrical Resistivity using ArtificialNeural Network Approach

Apaloo-Bara¹,

Salami²,

Kodjo³

et al. 2019

American Journal of Applied Sciences

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The knowledge of the ground electrical resistivity is essential to ensure the protection of electrical and telecommunications networks. However, the monitoring of its values is an expensive task which takes long time. Therefore, its prediction is important. This study investigates on predicting soil electrical resistivity using Artificial Neural Networks. Nine sites of our city (Lome, TOGO) were considered. After characterization of the resistivity data collected on these sites, two models have been developed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks. Relative Root Mean Square Error (RRMSE) and R 2 (Linear Correlation Coefficient) have been used to evaluate each model performance. For the MLP model, the configuration [ABCDEF] is the most efficient with the RRMSE = 12.00%, R 2 = 81.91% and 70 neurons under the hidden layer. For the RBF model, the configuration [BCDEF] is the most efficient with the RRMSE = 16.07%, R 2 = 69.97% and 100 neurons under the hidden layer. In general, the results exhibit that the MLP outcome configuration [ABCDEF] is the most efficient with the best RRMSE = 16.07% and R 2 = 69.97%. The letter A, B and C are the weather parameters and D, E, F are the geo-referenced coordinates of the measuring point. So far, research has not focused on predicting the electrical resistivity of the soil at a given location. Thus, the results of this study show that from meteorological data, it's possible to predict this electrical resistivity.

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An analysis of conventional grounding impedance based on the impulsive current distribution of a horizontal electrode

Cited by 11 publications

References 11 publications

The effect of soil ionization on transient grounding electrode resistance in non-homogeneous soil conditions

The effect of soil ionization on transient grounding electrode resistance in non-homogeneous soil conditions

Estimation of Soil Electrical Resistivity Using Mlp, Rbf, Anfis and SVM Approaches.

Estimation of Soils Electrical Resistivity using ArtificialNeural Network Approach

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