Estimation of seasonal variation of ground resistance using Artificial Neural Networks

Asimakopoulou, Fani E.; Tsekouras, George J.; Gonos, Ioannis F.; Stathopulos, I.A.

doi:10.1016/j.epsr.2012.07.018

Cited by 38 publications

(21 citation statements)

References 10 publications

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“…Both datasets were anteriorly published in F.E. Asimakopoulou et al [1][2][3] and also used as standard benchmark data.…”

Section: Experimental Datamentioning

confidence: 99%

“…The output feature is the ground resistance (in Ω). The soil resistivity and ground resistance quantifications of the soil resistivity were conducted in the area of Athens [1][2][3].…”

Section: Experimental Datamentioning

confidence: 99%

“…F.E. Asimakopoulou et al [1][2][3] have presaged the demeanor of the ground resistance of a single rod during a period of time by utilizing ANN that have been trained with experimental data of soil resistivity and weather conditions. On that purport a methodology for the optimization of the parameters of different training algorithms and the cull of the optimum algorithm has been implemented.…”

Section: Introductionmentioning

confidence: 99%

“…Soil resistivity measurements are often corrupted by the existence of ground currents and their harmonics. The soil resistivity and ground resistance quantifying procedure described in [1][2][3] utilizes the macrocosmically accepted Wenner method developed by Dr. Frank Wenner.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Both datasets were anteriorly published in F.E. Asimakopoulou et al [1][2][3] and also used as standard benchmark data.…”

Section: Experimental Datamentioning

confidence: 99%

“…The output feature is the ground resistance (in Ω). The soil resistivity and ground resistance quantifications of the soil resistivity were conducted in the area of Athens [1][2][3].…”

Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Untitled

2015

IJSC

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“…When there is lightning current or failure current in power grid, grounding grids provide a discharging channel to current [7], hence the conductive ability of each conductor of grounding grid is of great importance as the grounding grid is responsible for the stable operation of the power system, safety of the power the power equipment and the personnel working at power grid. Generally grounding grids are made up of iron or galvanized iron and corrosion can occur with the passage of time.…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-contact Measurement Method for the Detection of Current Flowing Through Concealed Conductors

Yang¹,

Li²,

Zhu³

et al. 2017

Journal of Magnetics

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In order to detect the current flowing through concealed conductor, this paper proposes a new method based on derivative method. Firstly, this paper analyzes the main peak characteristic of the derivative function of magnetic field generated by a current-carrying conductor, and a relationship between the current flowing through the conductor and the main peak of the derivative function is obtained and applied to calculate the current. Then, the method is applied to detect the conductor current flowing through grounding grids of substations. Finally, the numerical experimental and field experiment verified the feasibility and accuracy of the method, and the computing results show that the method can effectively measure the conductor current of grounding grids with low error, and the error is within 5 %.

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