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Wavelet neural network methodology for ground resistance forecasting

机译：小波神经网络方法用于地面电阻预测

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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 (5) 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. (C) 2016 Elsevier B.V. All rights reserved.
机译：由于工程师需要一种灵活，可靠的工具来估算和预测接地系统的行为，因此本研究开发了一个模型，该模型可以准确地描述和预测接地电阻变化的动态。众所周知，接地系统对于电气设施，变电站，输电线路以及一般的电力系统的安全运行而言是至关重要的。但是，在大多数情况下，在设计阶段，电气工程师和研究人员对有关地形的土壤电阻率变化的信息有限。此外，住宅和建筑物的基础设施经常阻碍接地电阻的定期测量。在本研究中开发的模型由一个非线性的，非参数的小波神经网络（WNN）组成，在过去四年中对土壤电阻率和降雨高度的野外测量进行了训练。所提出的框架已在五（5）个具有不同接地增强剂的不同接地系统中进行了测试，因此可用于评估几种接地实践中经常使用的接地增强剂的性能。研究结果表明，WNN可以构成用于接地电阻预测的准确模型，并且可以作为处置电气工程师的有用工具。因此，本文在接地电阻评估领域介绍了小波分析，并努力利用计算智能的优势。 （C）2016 Elsevier B.V.保留所有权利。

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