Науковий вісник НГУ

Knowledge base formation for automation of dispatch control over power systems of the mining and metallurgical complex

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №4 2021

Last Updated on 23 August 2021

Published on 30 November -0001

Hits: 5788

Authors:

V.S.Morkun, orcid.org/0000-0003-1506-9759, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.A.Kotov, orcid.org/0000-0003-2445-6259, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (4): 103 - 109

https://doi.org/10.33271/nvngu/2021-4/103

Abstract:

Purpose. The research is aimed at developing and introducing methods of knowledge extraction concerning online control over power systems under emergency modes and building smart complexes of automatizing managerial decision making based on incorporated ontological knowledge bases.

Methodology. The authors use the calculated planned experiment method applied to building sensitivity matrices of controlled parameters of power systems in sensor points to controlled factors and introduction of sensitivity coefficients into knowledge bases.

Findings. The research suggests methods for obtaining and building a knowledgebase of professional ontologies for online control over power system modes. The problem of calculating sensitivity of controlled parameters to controlling actions is solved. Calculation results for the emergency mode enable building impact functions and determining sensitivity matrix coefficients. The smart system knowledgebase is built to provide decision support for dispatch control over power system modes under standard and emergency conditions. There are obtained sets of mode data used as knowledgebase components enabling efficient assessment of the emergency mode rate and its dispatch correction. Besides calculation parameters of intensity of controlling actions, the knowledgebase also comprises linguistic concepts, facts and rules of instructive dispatch materials. A knowledge base has been built on the basis of a subset of the linguistic corpus of concepts for the professional area of emergency response in the power system.

Originality. For the first time, there is suggested an approach to incorporating various linguistic knowledge forms represented by a single ontological model and numerical parameters of sensitivity of the power system mode to controlling actions into an integrated knowledgebase, which enables building effective smart systems of dispatch decision support and implementing them into the operating automatized dispatch control system.

Practical value. The ontological knowledgebase of online dispatch control is built that enables realizing a software complex of a decision support system aimed at automatizing online dispatch control over standard and emergency modes of power systems. Application of the suggested approach to building the knowledgebase and its use with online dispatch personnels decision support enhance reliability and increase maximum accessible time of personnels non-stop work by 1.5 years with absolute accident elimination, thus providing a significant economic effect.

Keywords: emergency, dispatcher, ontology, decision support, thesaurus, power system

References.

1.Sandkuhl, K., & Smirnov, A. (2018). Knowledge Management in Production Networks: Classification of Knowledge Reuse Techniques. SPIIRAS Proceedings, 1(56), 5. https://doi.org/10.15622/sp.56.1.

2. Gavrilova, T.A., Kudryavtsev, D.V., & Kuznetsova, A.V. (2019). Choosing knowledge management methods and tools considering specific domain. Innovation, 8(250), 44-52. https://doi.org/10.26310/2071-3010.2019.250.8.007.

3. Chirapurath, J. (2019). Knowledge mining. The Next Wave of Artificial Intelligence-Led Transformation (1^st ed.). Harvard Business Publishing.

4. Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal Of Industrial Information Integration, 9, 1-13. https://doi.org/10.1016/j.jii.2017.08.001.

5. Massel, L., Vorozhtsova, T., & Pjatkova, N. (2017). Ontology engineering to support strategic decision-making in the energy sector. Ontology of Designing, 7(1), 66-76. https://doi.org/10.18287/2223-9537-2017-7-1-66-76.

6. Zagorulko, G.B. (2016). Development of ontology for intelligent scientific internet resource decision-making support in weakly formalized domains. Ontology of Designing, 22(4), 485-500. https://doi.org/10.18287/2223-9537-2016-6-4-485-500.

7. Massel, L. (2016). Fractal approach to knowledge structuring and examples of its application. Ontology of Designing, 6(2), 149-161 https://doi.org/10.18287/2223-9537-2016-6-2-149-161.

8. Ahmadeeva, I.R. (2018). Application of ontology to automatizing scientific data search in the Internet. Information and mathematical technologies in science and management, 4(12), 42-49. https://doi.org/10.25729/2413-0133-2018-4-12-42-49.

9. Morkun, V., Semerikov, S., Hryshchenko, S., & Slovak, K. (2017). Environmental Geo-information Technologies as a Tool of Pre-service Mining Engineers Training for Sustainable Development of Mining Industry. In13^th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI, 2017),(pp. 303-310). Kyiv, Ukraine; CEUR Workshop Proceedings (CEURWS.org.) Retrieved from http://ceur-ws.org/Vol-1844/10000303.pdf .

10. Morkun, V., & Kotov, I. (2019). Intellectualization of Emergency Control of Power Systems on the Basis of Incorporated Ontologies of Knowledge-Bases. Acta Mechanica Et Automatica, 13(2), 86-94. https://doi.org/10.2478/ama-2019-0012.

11. Golik, V., Komashchenko, V., Morkun, V., Morkun, N., & Hryshchenko, S. (2018). Energy Saving in Mining Production. Science and Innovation, 14(3), 29-39. https://doi.org/10.15407/scine14.03.029.

12. Madrigal, M., Uluski, R., & Mensan Gaba, K. (2017). Practical Guidance for Defining a Smart Grid Modernization Strategy: The Case of Distribution. World Bank Studies;.Washington, DC: World Bank (Revised Edition), 179. https://doi.org/10.1596/978-1-4648-1054-1.

13. Lyubarskiy, Yu.Ya., & Hrennikov, A.Yu. (2019). Computer support of dispatch decisions in electric grids. Library of electric engineers, Energoprogress, 8(248), 1-92. eLIBRARY ID:39215583.

14. DTEK Dniprovski Elektromerezhi (2020). Development plan for the distribution system of Corp. DTEK Dniprovski Elektromerezhi for the period 20202024. Retrieved from https://www.dtek-dnem.com.ua/ua/file/aCizMwe58AFkg ?inline=1.

15.Kozhikhova, O. (2017). Evaluation of the sensitivity of power flows to the parameters of the steady mode model. In VIII International Youth Scientific and Technical Conference Electric power industry through the eyes of youth 2017,(pp. 137-140). Samara: Samara State Technical University.

• < Prev
• Next >