Abstract
This paper proposes ways for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds. Grounding systems are used to divert high fault currents to the earth. The proper estimation of the ground resistance is useful from a technical and also economic viewpoint, for the proper electrical installation of constructions. The work utilizes both, conventional and intelligent data analysis techniques, for ground resistance modelling from field measurements. In order to estimate ground resistance from weather and ground data such as soil resistivity, rainfall measurements, etc., three linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based in feed-forward neural networks,. A feature selection process has also been successfully applied, showing that features selected for estimation agree with experts’ opinion on the importance of the variables considered. Experimental data consist of field measurements that have been performed in Greece during the last three years. The input variables used for analysis are related to soil resistivity within various depths and rainfall height during some periods of time, like last week and last month. Experiments produce high quality results, as correlation exceeds 99% for specific experimental settings of all approaches tested.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
ANSI/IEEE Std 80-2000: IEEE Guide for safety in AC substation grounding (2000)
ANSI/IEEE Std 81-2012: IEEE guide for measuring earth resistivity, ground impedance, and earth surface potentials of a grounding system (December 28, 2012)
Salam, M.A., Al-Alawi, S.M., Maquashi, A.A.: An artificial neural networks approach to model and predict the relationship between the grounding resistance and the length of the buried electrode in soil. Journal of Electrostatics 64, 338–342 (2006)
Amaral, F.C.L., De Souza, A.N., Zago, M.G.: A novel approach to model grounding systems considering the influence of high frequencies. In: Proc. of 5th Latin-American Congress on Electricity Generation & Transmission (CLAGTEE 2003), Sao Pedro, Brasil, November 16-20 (2003)
Gouda, O.E., Amer, M.G., El Saied, M.T.: Optimum design of grounding systems in uniform and non-uniform soils using ANN. International Journal of Soft Computing 1(3), 175–180 (2006)
Androvitsaneas, V.P., Asimakopoulou, F.E., Gonos, I.F., Stathopulos, I.A.: Estimation of ground enhancing compound performance using artificial neural network. In: Proceedings of 3rd International Conference on High Voltage Engineering and Application, Shanghai, China, September 17-20, pp. 174–178 (2012)
Asimakopoulou, F.E., Tsekouras, G.J., Gonos, I.F., Stathopulos, I.A.: Estimation of seasonal variation of ground resistance using artificial neural networks. Electric Power Systems Research 94(1), 113–121 (2013)
Tsekouras, G.J., Kanellos, F.D., Kontargyri, V.T., et al.: A comparison of artificial neural networks algorithms for short term load forecasting in greek intercontinental power system. In: Proceedings of 7th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal (CSECS 2008), Puerto De La Cruz, Canary Islands, Spain, December 15-17, pp. 108–115 (2008)
Asimakopoulou, G.E., Kontargyri, V.T., Tsekouras, G.J., et al.: Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators. IET Science, Measurement & Technology 3(1), 90–104 (2009)
Androvitsaneas, V.P., Gonos, I.F., Stathopulos, I.A.: Performance of ground enhancing compounds during the year. In: Proceedings of 31st International Conference on Lightning Protection, Vienna, Austria, September 2-7, pp. 231-1–231-5 (2012)
Guerard Jr., J.B.: Regression Analysis and Forecasting Models. In: Guerard Jr., J.B. (ed.) Introduction to Financial Forecasting in Investment Analysis, ch. 2. Springer Science + Business Media, New York (2013)
Zhang, G.P.: Neural Networks for Time-Series Forecasting. In: Rosenberg, et al. (eds.) Handbook of Natural Computing. Springer, Heidelberg (2012)
Liu, H., Hiroshi, M.: Feature Selection for Knowledge Discovery and Data Mining. The Springer International Series in Engineering and Computer Science, vol. 454 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Eleftheriadou, T., Ampazis, N., Androvitsaneas, V.P., Gonos, I.F., Dounias, G., Stathopulos, I.A. (2014). Ground Resistance Estimation Using Feed-Forward Neural Networks, Linear Regression and Feature Selection Models. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-07064-3_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
Online ISBN: 978-3-319-07064-3
eBook Packages: Computer ScienceComputer Science (R0)