Implementation of energy flow control of underground consumers in an iron ore mine
DOI:
https://doi.org/10.15588/1607-6761-2024-1-1Keywords:
Canopy algorithm, Hadoop platform, energy flow management, underground consumers, smart grid, iron ore mineAbstract
Purpose. To enhance the capabilities of the power flow control algorithm to minimize the level of electricity consumption in the electric power system of an iron ore mine, this includes underground consumers.
Methodology. The research was conducted using the following methods: fuzzy attribute reduction of a coarse set, attribute reduction by QuickReduct, K-means, and the Hadoop platform.
Findings. The article considers and describes the methodology for implementing an algorithm for minimizing the levels of electricity consumption for underground consumers of an iron ore mine. An algorithm implementation of the Kanopy algorithm using the fast calculation function has been developed and improved.
Classification using the K-means method and its implementation in the basic Hadoop platform was carried out. An efficiently functioning and improved algorithm for source control has been built to minimize the volume of electricity consumption in underground consumers of an iron ore mine. The key advantage of this algorithm for practical application is its flexibility in operation: it provides several solution options, unlike typical mathematical methods, where only one solution option is offered to determine the sequence of solving the tasks and problems. This algorithm will allow its use with multiple methods for calculating key energy parameters, which will help reduce the excessive amount of data for calculating volumes given the uncertainty of energy consumption by underground consumers and avoid unnecessary calculation operations in a branched data structure with several solutions with a clear systematization.
Originality. The paper improves the practical implementation of the functioning algorithm, which allows increasing the accuracy and efficiency of calculations by eliminating excessive levels of power consumption by underground consumers of an iron ore mine.
Practical value. This research should be applied to the preventive assessment and analysis of the calculated volumes for reducing power consumption levels and their systematization using methods with a branched data structure for underground consumers of an iron ore mine. Two possible ways of further development and improvement of the state of energy and power equipment at mining enterprises (especially at an iron ore mine) are outlined.
References
Olіjnik Ju.S. (2016). Upravlіnnja energozbere-zhennjam ta energospozhivannjam na promislovih ta gospodars'kih pіdpriєmstvah. Research gate, 1, 88-89. UDK 621.311.
Kіjko, S.G., Druzhinіn, Є.A., Prohorov, V.O. (2020). Model' planuvannja energospozhivannja meta-lurgіjnogo pіdpriєmstva. Sistemi upravlіnnja, navіgacії ta zv’jazku, 1, 59, 27-32. doi: 10.26906/SUNZ.2020.1.027.
Sіnchuk, O.M., Kupіn A.І., Sіnchuk І.O., Baranovs'-ka M.L., Budnіkov K.І. Do rozrobki algoritmu ener-goefektivnogo keruvannja elektroenergetichnim kompleksom z rozdіlenoju generacієju elektrichnoї energії v umovah zalіzorudnih shaht, 2021. Vіsnik Krivorіz'kogo nacіonal'nogo unіversitetu, 53, s. 118-126, doi: 10.31721/2306-5451-2021-1-53-118-126.
Solovej O.І., Sitnik O.O., Rozen V.P. ta іn. Tehnіko – ekonomіchnі rozrahunki sistem elektropostachannja promislovih pіdpriєmstv. 2012,Cherkasi. ChDTU, 251.
Zhurahіvs'kij A.V., Zhezhelenko І.V. Optimіzacіja elektroenergetichnih sistem. 2000, L'vіv, Marіupol'. Vidavnictvo Priazovs'kogo derzhavnogo tehnіchnogo unіversitetu ISSN 1997-9266, 109.
Boyd, J (2013). An internet – inspired electricity Grid. Spectrum IEEE, 1, 12-13.
Huang, A., Heydt G., Dale, S., & Crow M. (2008). En-ergy internet – future renewable electric delivery and management (FREEDM) systems. IEEE Power Elec-tronics Society News letter, 4, 8-9.
Denysiuk, S., Sokolovskyi, P. (2018). Analysis of the variable generation function on the step of transition to intellectual networks Smart Grid. Electrification of transport, 15, 31-42.
Denysiuk S., Tarhonskyi V., Artemiev M. (2018). Lo-cal electrical energy systems with active consumer: methods of consumption and algorithm of their functioning. Power engineering: economics, tech-nique, ecology, 3, 7-22.
Strzelecki R., Benysek G. 2010. Power Electronics in Smart Electrical Energy Networks. Springer, 414.
Fu, P., Wang, N.L., Wang, L.G., Morosuk, T., Tsatsa-ronic, G.B. (2016). Performance degradation diagno-sis of thermal power plants: A method bases on ad-vanced exergy analysis.Energy Convers Manage-ment, 130, 219-229. CrossRef.
Arora, P., Varshney, S. (2016). Analysis of K – Means and K – Medoids algorithm for big data. Pro-cedia Computer Sciencce, 78, 507-512. CrossRef.
Gerhard, Z., Usman, H., Max, B., & Thomas, L. (2015). Sanitation and analysis of operation data in energy systems. Energies, 8, 12776-12794.
Hadayeghparast, S., Soltani Nejad, Farsangi, A., & Shayanfar, H. (2019). Day – ahead stochastic multi – objective economic/emission operational schedul-ing of a large scale virtual power plant. Energy, 172, 630-646.
Lukovic, S., Kaitovic, I., Bondi, U. (2015). Adopting system engineering methodology to virtual power systems design flow. Faculty of Informatics - Univer-sity of Lugano, 4, 1-8.
Quian, J., Wang , P.H., Li, L. (2007). Application of clustering algorithm in target – value analysis for boiler operating parameter. Proceeding CSEE, 27, 71-74.
Brand, E.L., Vosloo, V., Mathnews E. (2015). Auto-mated energy efficiency – syproject identification in the gold mining industry. Proceeding of the 13th Con-ference on the Industrial and Commercial use of En-ergy, 17-22. DOI: 10.1109/ICUE.2015.7280241.
Thillainathan, L., Dipti, S., Vanessa, K.W. (2014). Demand side management of smart grid: Load shift-ing and incentives. Journal of Renewable and Sus-tainable Energy, 6 (03), 31-36.
Moslem, U., Mohd F.R., Syahirah A.H. & Tan, C.K. (2018). A review on peak load shaving strategies. Re-newable and Sustainable Energy Reviews, 82, 3323-3332.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 V.P. Vlasyuk
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aCreative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
