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Wavelet Neural Network Methodology for Ground Resistance Forecasting

Androvitsaneas, V. P., Alexandridis, Antonis, Gonos, I. F., Dounias, G, Stathopoulos, I. A. (2016) Wavelet Neural Network Methodology for Ground Resistance Forecasting. Electric Power Systems Research, 140 . pp. 288-295. ISSN 0378-7796. (doi:10.1016/j.epsr.2016.06.013) (KAR id:55889)

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Motivated by the need of engineers for a flexible and reliable tool for estimating and predicting grounding systems behavior, this study developed a model that accurately describes and forecasts the dynamics of ground resistance variation. It is well-known that grounding systems are a key of high importance for the safe operation of electrical facilities, substations, transmission lines and, generally, electric power systems. Yet, in most cases, during the design stage, electrical engineers and researchers have limited information regarding the terrain’s soil resistivity variation. Moreover, the periodic measurement of ground resistance is hindered very often by the residence and building infrastructure. The model, developed in the present study, consists of a nonlinear, nonparametric Wavelet Neural Network (WNN), trained in field measurements of soil resistivity and rainfall height, observed the past four years. The proposed framework is tested in five different grounding systems with different ground enhancing compounds, so that can be used for the evaluation of the behavior of several ground enhancing compounds, frequently used in grounding practice. The research results indicate that the WNN can constitute an accurate model for ground resistance forecasting and can be a useful tool in the disposal of electrical engineers. Therefore, this paper introduces the wavelet analysis in the field of ground resistance evaluation and endeavors to take advantage of the benefits of computational intelligence.

Item Type: Article
DOI/Identification number: 10.1016/j.epsr.2016.06.013
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Antonis Alexandridis
Date Deposited: 12 Jun 2016 17:16 UTC
Last Modified: 16 Feb 2021 13:35 UTC
Resource URI: (The current URI for this page, for reference purposes)
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