An Artificial Neural Network Model Based on Experimental Measurements for Estimating the Grounding Resistance

Year 2022, Volume: 2 Issue: 1, 29 - 37, 16.02.2022

Ahmet Kayabaşı , Berat Yıldız , Selami Balcı

https://doi.org/10.54569/aair.1016850

Cited By: 1

Abstract

In grounding systems established in rocky and sandy lands where contact resistance with metal electrodes is high, contact resistance is generally the most critical parameter that changes the total grounding resistance value. Therefore, the nonlinear variation of the earth contact resistance according to the soil type cannot be taken into account in determining the grounding resistance with the traditional mathematical formulas given theoretically. This reduces the accuracy of grounding resistance determination. In this study, experimental measurements were made according to soil types and a data set was created. Then, to estimate the total grounding resistance of complex grounding systems, a classification was made using the multi-layer sensor (MLP) type ANN algorithm and the successful results were reported. Thus, according to the data set prepared based on experimental measurements, the proposed general classification algorithm approach can be applied to any grounding system. It presents a different technique from the previous literature as a pre-feasibility study for estimating the grounding resistance, especially before the grounding system installation, which is an early stage of the design process.

Keywords

Grounding systems, grounding resistance measurement, parameter classification, soil type

References

• Balcı S, Helvacı, ÖA. “Comparative simulation on the grounding grid system of a wind turbine with FEA software”, Journal of Energy Systems (2019) 148–157.
• “ANSI/IEEE Std 81-1983”, IEEE Guide for Measuring Earth Resistivity, Ground Impedance, and Earth Surface Potentials of a Ground System (1983).
• Asimakopoulou FE, Kourni EA, Kontargyri VT, Tsekouras GJ, Stathopulos IA. “Artificial neural network methodology for the estimation of ground resistance”, In Proc. 15th WSEAS International Conference on Systems (2011, July) 453–458.
• “EN 62561-7:2012”, Lightning Protection System Components (LPSC) – Part 7: Requirements for earthing enhancing compounds (2012, Jan.).
• Androvitsaneas VP, Gonos IF, Dounias GD, Stathopulos I. “Ground resistance estimation using inductive machine learning”, In 19th Int. Symp. High Voltage Engineering (2015, August). Pilsen, Czech Republic.
• Asimakopoulou FE, Tsekouras GJ, Gonos IF, Stathopulos IA. “Artificial neural network approach on the seasonal variation of soil resistance”, In 2011 7th Asia-Pacific International Conference on Lightning (2011, November). 794–799; doi: 10.1109/APL.2011.6110235.
• Boulas K, Androvitsaneas VP, Gonos IF, Dounias G, Stathopulos IA. “Ground resistance estimation using genetic programming”, In Proc. 5th Int. Symp. 27th National Conf. Operational Research (2016, June) 66–71. Greece, Athens.
• Androvitsaneas VP, Asimakopoulou FE, Gonos IF, Stathopulos IA. “Estimation of ground enhancing compound performance using artificial neural network”, In 2012 International Conference on High Voltage Engineering and Application (2012, September) 145–149; doi: 10.1109/ICHVE.2012.6357068.
• Androvitsaneas VP, Gonos IF, Stathopulos IA, Alexandridis AK, Dounias G. “Wavelet neural network for ground resistance estimation”, In 2014 ICHVE International Conference on High Voltage Engineering and Application (2014, September) 1–5; doi: 10.1109/ICHVE.2014.7035419.
• Denche G, Faleiro E, Asensio G, Moreno J. “An Estimator of the Resistance of Large Grounding Electrodes from Its Geometric Characterization”, Applied Sciences 10(22) (2020) 8162; doi: 10.3390/app10228162.
• Kondylis GP, Damianaki KD, Androvitsaneas VP, Gonos IF. “Simplified formulae method for estimating wind turbine generators ground resistance”, IEEE Transactions on Power Delivery 33(6) (2018) 2829–2836; doi: 10.1109/TPWRD.2018.2839061.
• Androvitsaneas VP, Alexandridis AK, Gonos IF, Dounias GD, Stathopulos IA. “Wavelet neural network methodology for ground resistance forecasting”, Electric Power Systems Research 140 (2016) 288–295; doi: 10.1016/j.epsr.2016.06.013.
• Faleiro E, Asensio G, Moreno J. “An estimate of the uncertainty in the grounding resistance of electrodes buried in two-layered soils with non-flat surface”, Energies 10(2) (2017) 176; doi: 10.3390/en10020176.
• Asimakopoulou FE, Kontargyri VT, Tsekouras GJ, Gonos IF, Stathopulos IA. “Estimation of the earth resistance by Artificial Neural Network model”, IEEE Transactions on Industry Applications 51(6) (2015) 5149–5158; doi: 10.1109/TIA.2015.2427114.
• İlisu İÖ, “Grounding Against Indirect Touch in Electrical Facilities”, Chamber of Electrical Engineers Continuing Education Center (EMO) EG/2010/1.
• Salam MA, Rahman QM, Ang SP, Wen F. “Soil resistivity and ground resistance for dry and wet soil”, Journal of Modern Power Systems and Clean Energy 5(2) (2017) 290-297.
• Zupan J. “Introduction to artificial neural network (ANN) methods: what they are and how to use them”, Acta Chimica Slovenica 41 (1994) 327–327.
• Uhrig RE. “Introduction to artificial neural networks”, In Proceedings of IECON'95-21st Annual Conference on IEEE Industrial Electronics 1 (1995, November) 33–37. IEEE.
• Kayabasi A, Yildiz B, Aslan MF, Durdu A. “Comparison of ELM and ANN on EMG signals obtained for control of robotic-hand”, In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (2018, June) 1–5. IEEE.
• Yılmaz I, Yuksek AG. “An example of artificial neural network (ANN) application for indirect estimation of rock parameters”, Rock Mechanics and Rock Engineering 41(5) (2008) 781–795.
• Benghanem M, Mellit A, Alamri SN. “ANN-based modeling and estimation of daily global solar radiation data: A case study”, Energy conversion and management 50(7) (2009) 1644–1655.
• Anuradha B, Reddy VV. “ANN for classification of cardiac arrhythmias”, ARPN Journal of Engineering and Applied Sciences 3(3) (2008) 1–6.
• Kumar V, Sachdeva J, Gupta I, Khandelwal N, Ahuja CK. “Classification of brain tumors using PCA-ANN”, In 2011 world congress on information and communication technologies (2011, December) 1079–1083. IEEE.
• Abiodun, OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. “State-of-the-art in artificial neural network applications: A survey”, Heliyon 4(11) (2018) e00938.
• Abiodun OI, Jantan A, Omolara AE, Dada KV, Umar AM, Linus OU, ... Kiru MU. “Comprehensive review of artificial neural network applications to pattern recognition”, IEEE Access 7 (2019) 158820-158846.