© The Institution of Engineering and Technology
The work presented in this study aims to develop a methodological approach for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds, using artificial neural networks (ANNs). The ANN training is based on field measurements that have been performed in Greece during last years. The methodology uses as input variables measurements of soil resistivity within various depths and of rainfall height during some periods of time, like last week and last month and estimates the ground resistance value of the tested rods, based on an ANN. This work comprises two scenarios in which, several ANN training algorithms are applied and an optimisation process is performed regarding the values of parameters, such as the number of neurons, the activation functions combination and so on. Each training algorithm is compared to the others, based on the coefficient of determination between the experimental and estimated values for the test set and the algorithm with the best results is highlighted for the estimation of ground resistance value, formed by the ground enhancing compounds under various weather conditions.
References
-
-
1)
-
23. Androvitsaneas, V.P., Asimakopoulou, F.E., Gonos, I.F., et al: ‘Estimation of ground enhancing compound performance using artificial neural network’. Proc. Third Int. Conf. on High Voltage Engineering and Application, Shanghai, China, September 2012, pp. 174–178.
-
2)
-
16. Hu, W., Yu, S., Cheng, R., et al: ‘A testing research on the effect of conductive backfill on reducing grounding resistance under lightning’. Proc. 31st Int. Conf. on Lightning Protection, Vienna, Austria, September 2012, pp. 155-1–155-4.
-
3)
-
18. 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’, J. Electrost., 2006, 64, pp. 338–342 (doi: 10.1016/j.elstat.2005.08.004).
-
4)
-
19. Amaral, F.C.L., De Souza, A.N., Zago, M.G.: ‘A novel approach to model grounding systems considering the influence of high frequencies’. Proc. Fifth Latin-American Cong. on Electricity Generation and Transmission, Sao Pedro, Brasil, November 2003.
-
5)
-
9. Gomes, C., Lalitha, C., Priyadarshanee, C.: ‘Improvement of earthing systems with backfill materials’. Proc. 30th Int. Conf. on Lightning Protection, Cagliari, Italy, September 2010, pp. 1086-1–1086-9.
-
6)
-
I.F. Gonos ,
I.A. Stathopoulos
.
Estimation of multilayer soil parameters using genetic algorithms.
IEEE Trans. Power Deliv.
,
1 ,
100 -
106
-
7)
-
D.W. Marquardt
.
An algorithm for least-squares estimation of non-linear parameters.
J. Soc. Ind. Appl. Math.
,
2 ,
431 -
441
-
8)
-
5. Gonos, I.F., Moronis, A.X., Stathopulos, I.A.: ‘Variation of soil resistivity and ground resistance during the year’. Proc. 28th Int. Conf. on Lightning Protection, Kanazawa, Japan, 2006, pp. 740–746.
-
9)
-
12. Arturo Galván, D., Gilberto Pretelin, G., Enrique Gaona, E.: ‘Practical evaluation of ground enhancing compounds for high soil resistivities’. Proc. 30th Int. Conf. on Lightning Protection, Cagliari, Italy, September 2010, pp. 1233-1–1233-4.
-
10)
-
13. Kokkinos, D., Kokkinos, N., Koutsoubis, J., et al: ‘High frequency behavior of soil improver compounds’. Proc. 30th Int. Conf. on Lightning Protection, Cagliari, Italy, September 2010, pp. 1400-1–1400-5.
-
11)
-
7. Shudha, K., Israil, M., Mittal, S., et al: ‘Soil characterization using electrical resistivity tomography and geotechnical investigations’, J. Appl. Geophys., 2009, 67, (1), pp. 74–79 (doi: 10.1016/j.jappgeo.2008.09.012).
-
12)
-
R. Battiti
.
First and second order methods for learning: between steepest descent and Newton's method.
Neural Comput.
,
2 ,
141 -
166
-
13)
-
6. Banton, O., Cimon, M.A., Seguin, M.K.: ‘Mapping field-scale physical properties of soil with electrical resistivity’, Soil Sci. Soc. Am. J., 1997, 61, (4), pp. 1010–1017 (doi: 10.2136/sssaj1997.03615995006100040003x).
-
14)
-
29. Kolmogorov, A.N.: ‘On the representation of continuous functions of several variables as superpositions of continuous functions of one variable and addition’, Dokladi Akademii Nauk USSR, 1957, 114, (5), pp. 953–956.
-
15)
-
3. Tagg, G.F.: ‘Earth resistances’ (George Newnes Limited, London, 1964).
-
16)
-
27. Levenberg, K.: ‘A method for the solution of certain problems in least squares’, Q. Appl. Math., 1944, 2, pp. 164–168.
-
17)
-
30. Kůrková, V.: ‘Kolmogorov's theorem and multilayer neural networks’ (Neural Networks Pergamon Press Ltd., 1992), vol. 5, pp. 501–506.
-
18)
-
8. Lianglu, L., Binquan, Q.: ‘Research on influence of soil water content on soil resistivity’. Proc. 31st Int. Conf. on Lightning Protection, Vienna, Austria, September 2012, pp. 24-1–24-7.
-
19)
-
20)
-
11. Wan Ahmad, W.F., Abdul Rahman, M.S., Jasni, J., et al: ‘Chemical enhancement materials for grounding purposes’. Proc. 30th Int. Conf. on Lightning Protection, Cagliari, Italy, September 2010, pp. 1106-1–1106-6.
-
21)
-
P.S. Ghosh ,
S. Chakravorti ,
N. Chatterjee
.
Estimation of time-to-flashover characteristics of contaminated electrolytic surfaces using a neural network.
IEEE Trans. Dielectr. Electr. Insul.
,
6 ,
1064 -
1074
-
22)
-
22. Asimakopoulou, F.E., Tsekouras, G.J., Gonos, I.F., et al: ‘Estimation of seasonal variation of ground resistance using artificial neural networks’, Electr. Power Syst. Res., 2012, 94, (1), pp. 113–121.
-
23)
-
20. Gouda, O.E., Amer, M.G., El Saied, M.T.: ‘Optimum design of grounding systems in uniform and non-uniform soils using ANN’, Int. J. Soft Comput., 2006, 1, (3), pp. 175–180.
-
24)
-
21. 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 Sci. Meas. Technol., 2009, 3, (1), pp. 90–104 (doi: 10.1049/iet-smt:20080009).
-
25)
-
31. Trenn, S.: ‘Multilayer perceptrons: approximation order and necessary number of hidden units’, IEEE Trans. Neural Netw., 2008, 19, (5), pp. 836–844 (doi: 10.1109/TNN.2007.912306).
-
26)
-
10. Jasni, J., Siow, L.K., Ab Kadir, M.Z.A., et al: ‘Natural materials as grounding filler for lightning protection system’. Proc. 30th Int. Conf. on Lightning Protection, Cagliari, Italy, September 2010, pp. 1101-1–1101-6.
-
27)
-
15. Lim, S.C., Gomes, C., Ab Kadir, M.Z.A., et al: ‘Preliminary results of the performance of grounding electrodes encased in bentonite-mixed concrete’. Proc. 31st Int. Conf. on Lightning Protection, Vienna, Austria, September 2012, pp. 145-1–145-5.
-
28)
-
29)
-
32. Haykin, S.: ‘Neural networks and learning machines: Pearson International Edition’ (Prentice-Hall-Pearson Education Inc., 2009, 3rd edn.).
-
30)
-
14. Laverde, V., Ab Kadir, M.Z.A., Gomes, C.: ‘Performance of backfill materials under impulse and AC testings’. Proc. 31st Int. Conf. on Lightning Protection, Vienna, Austria, September 2012, pp. 121-1–121-7.
-
31)
-
24. 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’. Proc. WSEAS Int. Conf. on Circuits, Systems, Electronics, Control & Signal (CSECS ‘08), Puerto De La Cruz, Canary Islands, Spain, December 2008, pp. 108–115.
-
32)
-
17. Androvitsaneas, V.P., Gonos, I.F., Stathopulos, I.A.: ‘Performance of ground enhancing compounds during the year’. Proc. 31st Int. Conf. on Lightning Protection, Vienna, Austria, September 2012, pp. 231-1–231-5.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2013.0292
Related content
content/journals/10.1049/iet-smt.2013.0292
pub_keyword,iet_inspecKeyword,pub_concept
6
6