A new approach on AI application for grounding resistor prediction in underground mines of Vietnam
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- Category: Content №5 2022
- Last Updated on 31 October 2022
- Published on 31 October 2022
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Authors:
Le Xuan Thanh, orcid.org/0000-0001-5052-4484, HaNoi University of Mining and Geology, HaNoi, the Socialist Republic of Vietnam
Ho Viet Bun, orcid.org/0000-0003-2123-9179, HaNoi University of Mining and Geology, HaNoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (5): 158 - 163
https://doi.org/10.33271/nvngu/2022-5/158
Abstract:
Purpose. To apply artificial intelligence (AI) technology for predicting the earthing resistor of underground mines with consideration of climate change parameters.
Methodology. In underground coal mines of Vietnam, the earthing system are everywhere equipped with individual rods combined with centralized grounding bed; this system significantly influences electrical safety and explosion safety. In daily operation, the resistor of the earthing system must be measured and tested regularly to ensure their value lower than the allowance limit (2). However, because of being affected by climate parameters (humidity and temperature in mines) this value varies frequently. By applying a new Neural Network with 3 hidden layers including variable parameters, the paper presents a new approach on predicting the earthing resistor. An algorithm is formed with visible and easily usable software for assisting the operator to predict the resistor. The prediction could be used for onsite management of a mine operator in the field of observing and testifying the earthing system in underground mines.
Findings. Software is developed based on AI technology for assisting the operator to predict the value of the earthing resistor corresponding to climate change.
Originality. Neural network with AI technology application is utilized relying on onsite measurements.
Practical value. Prediction results could be used in case of difficulty in measurement. It will also help to correct or eliminate the measurement error from a mining technician.
Keywords:660 V grid, Ai technology, earthing system, neuron network, underground mines
References.
1. VINAMCOMIN (2019). Orientation for sustainable development of coal mining approved by Prime-ministers Decision No. 403/2016/Q-TTg. Retrieved from http://www.vinacomin.vn/dinh-huong-phat-trien-ben-vung-nganh-than-viet-nam-gan-voi-dam-bao-an-ninh-nang-luong/dinh-huong-phat-trien-ben-vung-nganh-than-viet-nam-gan-voi-dam-bao-an-ninh-nang-luong-201901181507032041.htm.
2. Vietnam National regulation on safety Mining, QCVN 01:2011/BCT (2011). Retrieved from http://www.kiemdinh.vn/upload/files/QCVN%2001-2011-BCT%20An%20toa%CC%80n%20trong%20khai%20tha%CC%81c%20than%20h%C3%A2%CC%80m%20lo%CC%80.pdf.
3. Coal Mining Safety and Health Regulation 2017, Part 4 Electrical activities, equipment and installations (2017). Retrieved from https://www.legislation.qld.gov.au/view/pdf/asmade/sl-2017-0165.
4. Safety Standards for Electrical Installations and Equipment in explosives facilities, JSP 482 MOD Explosives Regulations (2016). Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/529168/20160526-JSP_482_Edt4_1_Chapter_8.pdf.
5. TCVN 6780-4, 2009, Ministry of Industry National Technical safety Regulation on Underground mines (n.d.). Retrieved from https://tieuchuan.vsqi.gov.vn/tieuchuan/view?sohieu=TCVN+6780-4%3A2009.
6. Niculescu, T., Arad, V., Marcu, M., Arad, S., & Popescu, F.G. (2020). Safety barrier of electrical equipment for environments with a potential explosion in underground coal mines. Mining of Mineral Deposits, 14(3), 78-86. https://doi.org/10.33271/mining14.03.078.
7. Androvitsaneas, V.P., Tsekouras, G.J., Gonos, I.F., & Stathopulos,I.A. (2014). Design of an artificial neural network for ground resistance forecasting. Power Generation, Transmission, Distribution and Energy Conversion, 1-6. https://doi.org/10.13140/RG.2.1.2509.6807.
8. ANSI/IEEE Std 80-2000: IEEE Guide for safety in AC substation grounding (2000). Retrieved from https://www.powerandcables.com/wp-content/uploads/2017/12/IEEE-Guide-for-Safety-In-AC-Substation-Earthing-Grounding.pdf.
9. ANSI/IEEE Std 81-2012: IEEE guide for measuring earth resistivity, ground impedance, and earth surface potentials of a grounding system (n.d.). Retrieved from https://standards.ieee.org/standard/81-2012.html.
10. Thanh, L.X., & Bun, H.V. (2021). Identifying the efficiency decrease factor of motors working under power harmornic in 660V electric mining grids. Mining of Mineral Deposits, 15(4), 108-113. https://doi.org/10.33271/mining15.04.108.
11. Thanh, L.X., & Bun, H.V. (2022). Identifying the factors influencing the voltage quality of 6 kV grids when using electric excavators in surface mining. Mining of Mineral Deposits, 16(2), 73-80. https://doi.org/10.33271/mining16.02.073.
12. Abrougui, K., Gabsi, K., Mercatoris, B., Khemis, C., Amami, R., & Chehaibi, S. (2019). Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil and Tillage Research, (190), 202-208. https://doi.org/10.1016/j.still.2019.01.011.
13. Amaral, F.C.L., de Souza, A.N., & Zago, M.G. (2003). A novel approach to model grounding systems considering the influence of high frequencies. Electricity Generation & Transmission, 1-9.
14. Gouda, O.E., Amer, G.M., & EL-Saied, T.M. (2006). Optimal design of grounding system in Uniform and Non-uniform soils using ANN. International Journal of Soft Computing, 1(3), 175-180.
15. He, J., Yu, G., Yuan, J., Zeng, R., Zhang, B., Zou, J., & Guan, Z. (2005). Decreasing grounding resistance of substation by deep-ground-well method. IEEE transactions on power delivery, 20(2), 738-744. https://doi.org/10.1109/TPWRD.2005.844301.
16. Asimakopoulou, F.E., Tsekouras, G.J., Gonos, I.F., & Stathopulos,I.A. (2013). Estimation of seasonal variation of ground resistance using artificial neural networks. Electric Power Systems Research, (94), 113-121. https://doi.org/10.1016/j.epsr.2012.07.018.
17. Asimakopoulou, F.E., Kourni, E.., Kontargyri, V.T., Tsekouras,G.J., & Stathopulos, I.A. (2013). Artificial Neural Network Methodology for estimation of ground resistance. Recent Researches in System Science, 453-458.
18. Anbazhagan, S. (2015). Athens seasonal variation of ground resistance prediction using neural networks. Journal on Soft Computing, 1113-1116. https://doi.org/10.21917/ijsc.2015.0154.
19. Ismujianto, Aji, A.D., Isdawimah, & Nadhiroh, N. (2019). Real-time Measurement for Controlling Grounding Resistance. Proceedings of the 8th Annual Southeast Asian International Seminar, 120-125. https://doi.org/10.5220/0009910801200125.
20. Selvam, A., & Manikandan, P. (2016). Performance analysis of Flyash with Bentonite in grounding pit. In 3rd International Conference on Electrical Energy Systems (ICEES), 58-61. https://doi.org/10.1109/ICEES.2016.7510616.
21. Ismujianto, Isdawimah, & Nadhiroh, N. (2019). Improvement of Electrical grounding system using bentonite. Journal of Physics: Conference Series, 1364(1), 012063. https://doi.org/10.1088/1742-6596/1364/1/012063.
22. Jais, I.K.M., Ismail, A.R., & Nisa, S.Q. (2019). Adam optimization algorithm for wide and deep neural network. Knowledge Engineering and Data Science, 2(1), 41-46. https://doi.org/10.17977/um018v2i12019p41-46.
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