Information technologies for power supply dispatch control based on linguistic corpus ontologies

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V.Morkun, Dr. Sc. (Tech.), Prof.,, State Institution of Higher Education “Kryvyi Rih National University”, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Kotov, Cand. Sc. (Tech.), Assoc. Prof.,, State Institution of Higher Education “Kryvyi Rih National University”, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

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Purpose. Developing methods and algorithms of smart decision support systems (DSS) and their implementation as a component of automated dispatch control systems (ADCS) in power systems.

Methodology. The unitized method is applied to presenting professional ontologies. A model of signal-flow labeling is offered to control knowledge.

Findings. The research results in a unitized approach to incorporating professional ontologies to build smart decision support systems and automate complex-structured objects, which is noted for implementing unity of forms of knowledge representation. All knowledge forms are described by a single model of ontologies, which enables unitizing the mechanism of representing and processing knowledge of grid modes in power systems. The mathematical apparatus of ontology representation and application is improved and differs from existing ones by the fact that it is based on fundamental forms of knowledge representation independent of subject areas. The basic model of an elementary signal-flow graph of the knowledge base is elaborated. There are introduced formalisms of the labeling parameter and functions of labeling the elementary signal-flow graph of the knowledge base. A factual knowledge base is built considering subsets of the linguistic corpus of accident elimination and prevention in power systems. A specialized thesaurus of professional terms and slang of accident elimination and prevention in power systems is built.

Otiginality. For the first time, the research suggests a unitized approach to representing and controlling professional knowledge of dispatch emergency control based on the signal-flow graph. This enables creating efficient decision-support systems and implementing them into the current ADCS.

Practical value. The authors suggest forming new ontologies of knowledge bases for the area of dispatch control modes in the power system as sensitivity matrices based on factorial mode models. There are developed integral factors of thesaurus efficiency enabling assessment of efficiency of various forms of professional knowledge representation. The operating information and control complex (OICC) which is a structural scheme of integrating the DSS into the ADCS is developed to monitor power system modes on the basis of the empirical knowledge base for mode characteristics of the power system. After generalizing the results of testing the software complex while training dispatch personnel of the power system, it can be claimed that improved professional and psychological characteristics of operating personnel, reduced intensity of dispatch failures and conditional damage because of power undersupply indicate practical relevance of applying the DSS to controlling the power system under emergency modes.


1. Antamoshin, A. N., Bliznova, O. V., Bobov, A. V., Bolshakov, A. A., Lobanov, V. V., & Kuznetsova, I. N. (2016). Smart systems of controlling organization and technical schemes. Moscow: Goryachaya liniya-Telekom. ISBN: 978-5-9912-0576-4.

2. Muhamedzhanova, Ye. R., & Akatyev, V. A. (2017). Analysis of large accidents at radiation entities and their impact on global atomic power engineering. Globalnaya yadernaya bezopasnost – Global security, 3(24), 110-114. ISSN: 2305-414X.

3. Barkalov, S. A., Dushkin, A. V., Kolodyazhnyi, S. A., & Sumin, V. I. (2017). Introduction to system design of smart knowledge bases: monograph. Moscow: Goryachaya liniya – Telekom. ISBN: 978-5-9912-0589-4.

4. Cepeda-Carrion, I., Martelo-Landroguez, S., Leal-Rodríguez, A. L., & Leal-Millán, A. (2017). Critical processes of knowledge management: An approach toward the creation of customer value, European Research on Management and Business Economics, 23(1), 1-7.

5. Iskandar, К., Jambak, M. I., Kosalaa, R., & Prabowo, H. (2017). Current Issue on Knowledge Management Systemfor future research: a Systematic Literature Review. 2nd International Conference on Computer Science and Computational Intelligence 2017. ICCSCI 2017, 13-14 October 2017, Bali, Indonesia, Procedia Computer Science, 116(2017), (pp. 68-80).

6. Cerchione, R., & Esposito, E. (2017).Using knowledge management systems: A taxonomy of SME strategies. International Journal of Information Management, 37, 1551-1562.

7. Arutyunyan, R. V., Bolshov, L. A., Borovoy, A. A., & Velihov, E. P. (2018). System analysis of causes and consequences of the accident at NPP Fukusima-1. Moscow: In-t problem bezopasnogo razvitiya atomnoy energetiki RAN. ISBN 978-5-9907220-5-7.

8. Morkun, V., Morkun, N., & Tron, V. (2015). Model synthesis of nonlinear nonstationary dynamical systems in concentrating production using Volterra kernel transformation. Metallurgical and Mining Industry7(10),  6-9.

9. Golik, V., Komaschenko, V., Morkun, V., & Khasheva, Z. (2015). The effectiveness of combining the stages of ore fields development. Metallurgical and Mining Industry7(5), 401-405.

10. Golik, V., Komashchenko, V., & Morkun, V. (2015).  Geomechanical terms of use of the mill tailings for preparation. Metallurgical and Mining Industry7(4),  321-324.

11. Sato, T., Kammen, D.M., Duan, B., Macuha, M., Zhou, Z., Wu, J., Tariq, M., & Asfaw, A.S. (2015). Smart Grid Standards: Specifications, Requirements, and Technologies. John Wiley & Sons Singapore Pte. ISBN: 978-1-118-65369-2.

12. Kasarpatil, K., Karadkar, T., & Dudwadkar, A. (2015). Digital Substation and Case Study of Tata Power // International Journal of Emerging Technology and Advanced Engineering, 5(2),134-141.

13. Miah, S.J., & Genemo, H. (2016). A Design Science Research Methodology for Expert Systems Development. Australasian Journal of Information Systems, 20(0), 1-29.

14. Duer, S., Wrzesień, P., & Duer, R. (2017). Creating of structure of facts for the knowledge base of an expert system for wind power plant’s equipment diagnosis.Web of Conferences, 1,

15. Xamena, E., Brignole, N. B.,& Maguitman, A. G. (2017). A Structural Analysis of topic ontologies. Information Sciences, 421, 15-29.

16. Moschytz, G.S. (2019). An Introduction to Signal-Flow Graph Theory. In Analog Circuit Theory and Filter Design in the Digital World (pp. 85-100). Springer, Cham.

Tags: ontologyknowledge basesignal-flow graphformal languagepower system

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