Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines

Abstract:

For many high voltage transmission lines, lightning is the first cause of outages. Different alternatives have been used to diminish these outages, like the use of counterpoise wires, installation of surge arresters, and the improvement of the grounding system using ground enhancers or chemical enhancers. In this paper, graphene nanoparticles were used to reformulate commercial ground enhancers. The results of this research end up in an improvement factor of up to 100 times the reduction in resistivity, when graphene nanoparticles are used. After lightning current impulse tests done on both types of samples, the performance of the un-reformulated ground enhancer samples shows a faster deterioration than the graphene reinforced ground enhancer samples. In order to establish a criterion to quantitatively rank the chemical ground enhancers ́ performance after the lightning impulse current tests, a neural network model was developed.

• Periodical: Journal of Nano Research (Volume 58)
• Pages: 139-150
• DOI: https://doi.org/10.4028/www.scientific.net/JNanoR.58.139
• Online since: June 2019
• Authors: Isaias Ramirez-Vazquez*, Miguel Ramirez-Gonzalez, J. Eduardo Salgado-Talavera
• Keywords: Artificial Neural Networks, Graphene, Ground Enhancer, Impulse Lightning Current Test, Lightning, Nanoparticles, Soil Resistivity, Transmission Lines

References

[1] C.F.E., Summary of failures in transmission lines 2010-2014, México, D.F., 2015 (in Spanish).

[2] C.F.E., Outages, Statistics, and Relevant Works in Transmission Lines 2011, México, D.F., January 2012 (in Spanish).

[3] C.F.E., Annual Report 2011, México, D.F., 2011 (in Spanish).

[4] C.F.E., Report of the Federal Public Administration 2006–2012, México, D.F., 2013 (in Spanish).

[5] J.M. Tobias, The basis of conventional lightning protection systems, IEEE Transactions on Industry Applications. 40 (2004) 958- 962.

DOI: 10.1109/tia.2004.831277

[6] T.T. Nguyen and R. Holt, Lightning protection of transmission lines: optimal shielding design procedure, IEE Proceedings-Generation, Transmission and Distribution. 150 (2003) 659-667.

DOI: 10.1049/ip-gtd:20030819

[7] W.A. Chisholm, E. Petrache and F. Bologna, Grounding of overhead transmission lines for improved lightning protection, IEEE PES Transmission and Distribution Conference and Exposition. , (2010) 1-6.

DOI: 10.1109/tdc.2010.5484321

[8] K. Munukutla, V. Vittal, G.T. Heydt, D. Chipman and B. Keel, A practical Evaluation of Surge Arrester Placement for Transmission Line Lightning Protection, IEEE Transactions on Power Delivery. 25 (2010) 1742-1748.

DOI: 10.1109/tpwrd.2010.2040843

[9] F. Zhenghua, L. Ling and F. Junzhong, Research on reducing grounding resistance of transmission line tower grounding grid. International Conference on Electrical and Control Engineering (ICECE) (2011) 1216–1219.

DOI: 10.1109/iceceng.2011.6057902

[10] V. P. Androvitsaneas, I. F. Gonos, I. A. Stathopulos, Performance of Ground Enhancing Compounds During the Year, International Conference on Lightning Protection (ICLP). (2012) Vienna, Austria.

DOI: 10.1109/iclp.2012.6344356

[11] W.F. Wan Ahmad, M. S. Abdul Rahman, J. Jasni, M. Z. A. Ab Kadir and H. Hizam, Chemical Enhancement Materials for Grounding Purposes. 30th International Conference on Lightning Protection (ICLP), (2010) 1106-1, 1106-6, Cagliari, Italy.

DOI: 10.1109/iclp.2010.7845836

[12] Y. Zhu, S. Murali, W. Cai, X. Li, J.W. Suk, J.R. Potts and R.S. Ruoff, Graphene and Graphene Oxide: Synthesis, Properties, and Applications. Advanced Materials 22 (2010) 3906–3924.

DOI: 10.1002/adma.201001068

[13] Z.S. Wu, G. Zhou, L.C. Yin, W. Ren, F. Li, H.M. Cheng, Graphene/metal oxide composite electrode materials for energy storage. Nano Energy 1 (2012) 107–131.

DOI: 10.1016/j.nanoen.2011.11.001

[14] K.S. Novoselov, V.I. Falko, L. Colombo, P.R. Gellert, M.G. Schwab and K. Kim, A roadmap for graphene. Nature 490 (2012) 192-200.

DOI: 10.1038/nature11458

[15] D. Krishnan, F. Kim, J. Luo, R. Cruz-Silva, L.J. Cote, H.D. Jang and J. Huang, Energetic graphene oxide: Challenges and opportunities. Nano Today 7 (2012)137-152.

DOI: 10.1016/j.nantod.2012.02.003

[16] ASTM G 187-05, Standard Test Method for Measurement of Soil Resistivity Using the Two-Electrode Soil Box Method (2005).

DOI: 10.1520/g0187

[17] S. Sumathi and S. Paneerselvam, Computational intelligence paradigms: theory & applications using MATLAB, 1st Ed., Taylor & Francis Group, Florida, (2010).

[18] S. Samarasinghe, Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition, 1st Ed.,Taylor & Francis Group, Florida, (2007).

[19] T. Lin, B. G. Horne, P. Tiño and C.L. Giles, Learning long-term dependencies in NARX recurrent neural networks, IEEE Trans. on Neural Networks. 7 (1996) 1329-1338.

DOI: 10.1109/72.548162

[20] F.D. Foresee and M.T. Hagan, Gauss-Newton approximation to Bayesian regularization. Proc. International Joint Conference on Neural Networks (1997) 1930-1935.

DOI: 10.1109/icnn.1997.614194

[21] K. Narendra and K. Parthasarathy, Identification and control of dynamic systems using neural networks, IEEE Trans. on Neural Networks. 1 (1990) 4-27 (1990).

DOI: 10.1109/72.80202