Wavelet neural network methodology for ground resistance forecasting
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- 作者：Vasilios P. Androvitsaneasa ; v.andro@mail.ntua.gr" class="auth_mail" title="E-mail the corresponding author ; Antonios K. Alexandridisb ; A.Alexandridis@kent.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Ioannis F. Gonosa ; igonos@cs.ntua.gr" class="auth_mail" title="E-mail the corresponding author ; Georgios D. Douniasc ; g.dounias@aegean.gr" class="auth_mail" title="E-mail the corresponding author ; Ioannis A. Stathopulosa ; stathop@power.ece.ntua.gr" class="auth_mail" title="E-mail the corresponding author
- 关键词：Grounding systems ; Ground enhancing compounds ; Ground resistance ; Wavelet neural networks ; Forecasting ; Computational intelligence
- 刊名：Electric Power Systems Research
- 出版年：2016
- 出版时间：November 2016
- 年：2016
- 卷：140
- 期：Complete
- 页码：288-295
- 全文大小：2319 K
文摘
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id="par0005">A novel WNN forecasting model for ground resistance is proposed in this study.
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id="par0010">A WNN trained in field measurements of soil resistivity and rainfall height.
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id="par0015">It is tested in 5 different grounding systems with ground enhancing compounds.
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id="par0020">Information about ground enhancing compounds behavior and performance is reported.
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id="par0025">WNN model is proved to be an accurate tool for ground resistance forecasting.