Wavelet Neural Network for ground resistance estimation

This paper presents the results of a computational approach for the ground resistance of grounding systems, used for the safe operation of electrical installations, substations and power transmission lines and aspires to build a forecasting model for the ground resistance values. The proposed model consists of a Wavelet Neural Network, which has been trained and validated by field measurements, performed for the last three years. Several grounding rods, encased in ground enhancing compounds and natural soil, have been tested, so that a wide data set for the training of the network can be obtained, covering various soil conditions. The input variables of the network are the soil resistivity within various depths of the tested field, varying with respect to time and the rainfall height during the year. This work introduces the wavelet analysis in the field of ground resistance estimation and attempts to take advantage of the benefits of artificial intelligence.