Improving the ship's power plant automatic control system by using a model-oriented decision support system in order to reduce accident rate under the transitional and dynamic modes of operation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.234447

Keywords:

model-oriented system, method to improve accident rate and performance, accident rate, performance

Abstract

This paper proposes a method to improve the performance of a ship's power plant by reducing accidents within it under transitional operating modes. The method is based on decreasing the number of service personnel errors by using a model-oriented decision support system. In order to implement the proposed method, the structure of the system of automatic control of the ship's power plant has been improved. Such an improvement of the control system implied the integration of a modeling unit and a decision support unit into its structure. The modeling unit makes it possible to predict values of the controlled parameters under a transition mode of operation before they actually appear in the system as a result of the operator's actions. A mathematical model of the automatic control system under transitional operating modes has been built for this unit. In order to implement the decision support unit, a method has been devised to formalize the task of managing the power plant under transitional operating modes. The method essentially involves modeling a transitional operating regime, followed by an evaluation of the results based on regulatory requirements and an empirical criterion for assessing the quality of enabling the diesel generators to work in parallel. In addition, a method has been developed for the decision support unit to reduce the accident rate and improve performance with the help of a mathematical apparatus of fuzzy inference, fuzzy logic, and fuzzy sets. Transitional operating regimes resulting from actual erroneous operator actions during ship flights were investigated. As a result of using the proposed system, the power plant performance increases

Author Biographies

Igor Voytetsky, National University "Odessa Maritime Academy"

Senior Lecturer

Department of Marine Power Plants Automation

Taisiya Voytetskaya, Odessа Polytechnic State University

PhD

Department of Computer Technologies of Automation

Leonid Vyshnevskyi, National University "Odessa Maritime Academy"

Doctor of Technical Sciences, Professor

Department of Marine Power Plants Automation

Igor Kozyryev, National University "Odessa Maritime Academy"

PhD

Department of Marine Power Plants Automation

Oksana Maksymova, Naval Institute of the National University "Odessa Maritime Academy"

PhD, Associate Professor, Leading Researcher

Scientific Center

Maksym Maksymov, Odessа Polytechnic State University

Doctor of Technical Sciences, Professor, Head of Department

Department of Computer Technologies of Automation

Viktoriia Kryvda, Odessа Polytechnic State University

PhD, Associate Professor, Head of Graduate School

Department of Postgraduate and Doctoral Studies

References

  1. Burmeister, H.-C., Bruhn, W., Rødseth, Ø. J., Porathe, T. (2014). Autonomous Unmanned Merchant Vessel and its Contribution towards the e-Navigation Implementation: The MUNIN Perspective. International Journal of e-Navigation and Maritime Economy, 1, 1–13. doi: https://doi.org/10.1016/j.enavi.2014.12.002
  2. Zubowicz, T., Armiński, K., Witkowska, A., Śmierzchalski, R. (2019). Marine autonomous surface ship - control system configuration. IFAC-PapersOnLine, 52 (8), 409–415. doi: https://doi.org/10.1016/j.ifacol.2019.08.100
  3. Utne, I. B., Schjølberg, I., Roe, E. (2019). High reliability management and control operator risks in autonomous marine systems and operations. Ocean Engineering, 171, 399–416. doi: https://doi.org/10.1016/j.oceaneng.2018.11.034
  4. Hughes, G., Kornowa-Weichel, M. (2004). Whose fault is it anyway?: A practical illustration of human factors in process safety. Journal of Hazardous Materials, 115 (1-3), 127–132. doi: https://doi.org/10.1016/j.jhazmat.2004.06.005
  5. Papalambrou, G., Samokhin, S., Topaloglou, S., Planakis, N., Kyrtatos, N., Zenger, K. (2017). Model predictive control for hybrid diesel-electric marine propulsion. IFAC-PapersOnLine, 50 (1), 11064–11069. doi: https://doi.org/10.1016/j.ifacol.2017.08.2488
  6. Valdez Banda, O. A., Kannos, S., Goerlandt, F., van Gelder, P. H. A. J. M., Bergström, M., Kujala, P. (2019). A systemic hazard analysis and management process for the concept design phase of an autonomous vessel. Reliability Engineering & System Safety, 191, 106584. doi: https://doi.org/10.1016/j.ress.2019.106584
  7. Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z., Yan, X. (2020). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS. Ocean Engineering, 210, 107544. doi: https://doi.org/10.1016/j.oceaneng.2020.107544
  8. Sujesh, G., Ramesh, S. (2018). Modeling and control of diesel engines: A systematic review. Alexandria Engineering Journal, 57 (4), 4033–4048. doi: https://doi.org/10.1016/j.aej.2018.02.011
  9. Boldea, I., Tutelea, L. (2018). Reluctance Electric Machines: Design and Control. CRC Press, 430. doi: https://doi.org/10.1201/9780429458316
  10. Pelykh, S. N., Maksimov, M. V., Baskakov, V. E. (2008). Model of cladding failure estimation under multiple cyclic reactor power changes. Paper presented at the 2nd International Conference on Current Problems in Nuclear Physics and Atomic Energy, NPAE 2008 - Proceedings, 638–641.
  11. Jeong, B., Oguz, E., Wang, H., Zhou, P. (2018). Multi-criteria decision-making for marine propulsion: Hybrid, diesel electric and diesel mechanical systems from cost-environment-risk perspectives. Applied Energy, 230, 1065–1081. doi: https://doi.org/10.1016/j.apenergy.2018.09.074
  12. Coraddu, A., Oneto, L., Navas de Maya, B., Kurt, R. (2020). Determining the most influential human factors in maritime accidents: A data-driven approach. Ocean Engineering, 211, 107588. doi: https://doi.org/10.1016/j.oceaneng.2020.107588
  13. Qiao, W., Liu, Y., Ma, X., Liu, Y. (2020). A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS. Ocean Engineering, 197, 106892. doi: https://doi.org/10.1016/j.oceaneng.2019.106892
  14. Endrina, N., Konovessis, D., Sourina, O., Krishnan, G. (2019). Influence of ship design and operational factors on human performance and evaluation of effects and sensitivity using risk models. Ocean Engineering, 184, 143–158. doi: https://doi.org/10.1016/j.oceaneng.2019.05.001
  15. Baykov, A., Dar’enkov, A., Kurkin, A., Sosnina, E. (2019). Mathematical modelling of a tidal power station with diesel and wind units. Journal of King Saud University - Science, 31 (4), 1491–1498. doi: https://doi.org/10.1016/j.jksus.2019.01.009
  16. Kundur, P. (1993). Power system stability and control. McGraw-Hill Inc., 1200.
  17. Boldea, I. (2020). Induction Machines Handbook: Steady State Modeling and Performance. CRC Press, 443. doi: https://doi.org/10.1201/9781003033417
  18. Brunetkin, A. I., Maksimov, M. V. (2015). The method for determination of a combustible gase composition during its combustion. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 5, 83–90.
  19. Dahl, A. R., Thorat, L., Skjetne, R. (2018). Model Predictive Control of Marine Vessel Power System by Use of Structure Preserving Model. IFAC-PapersOnLine, 51 (29), 335–340. doi: https://doi.org/10.1016/j.ifacol.2018.09.501
  20. Skjong, S., Pedersen, E. (2017). A real-time simulator framework for marine power plants with weak power grids. Mechatronics, 47, 24–36. doi: https://doi.org/10.1016/j.mechatronics.2017.09.001
  21. Thorat, L., Skjetne, R. (2017). Load-dependent start-stop of gensets modeled as a hybrid dynamical system. IFAC-PapersOnLine, 50 (1), 9321–9328. doi: https://doi.org/10.1016/j.ifacol.2017.08.1180
  22. Li, W., Li, H., Gu, S., Chen, T. (2020). Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities. Control Engineering Practice, 105, 104637. doi: https://doi.org/10.1016/j.conengprac.2020.104637
  23. Pelykh, S. N., Maksimov, M. V., Nikolsky, M. V. (2014). A method for minimization of cladding failure parameter accumulation probability in VVER fuel elements. Problems of Atomic Science and Technology, 4 (92), 108–116.
  24. Vishnevskiy, L., Voytetskiy, I., Voytetskaya, T. (2019). Using model-oriented decision-making support system for the improvement of safe operation of a ship electric power installation. Computational Problems of Electrical Engineering, 9 (1), 37–43. Available at: http://science.lpnu.ua/jcpee/all-volumes-and-issues/volume-9-number-1-2019/using-model-oriented-decision-making-support
  25. Vishnevsky, L., Voytetsky, I., Voytetskaya, T. (2019). Marine Electrical Power Plant Dynamic Modes Evaluation Using a Fuzzy Inference System. 2019 IEEE 20th International Conference on Computational Problems of Electrical Engineering (CPEE). doi: https://doi.org/10.1109/cpee47179.2019.8949175

Downloads

Published

2021-06-30

How to Cite

Voytetsky, I., Voytetskaya, T., Vyshnevskyi, L., Kozyryev, I., Maksymova, O., Maksymov, M., & Kryvda, V. (2021). Improving the ship’s power plant automatic control system by using a model-oriented decision support system in order to reduce accident rate under the transitional and dynamic modes of operation . Eastern-European Journal of Enterprise Technologies, 3(2 (111), 57–66. https://doi.org/10.15587/1729-4061.2021.234447

Issue

Vol. 3 No. 2 (111) (2021): Information technology. Industry control systems

Section

Industry control systems

License

Copyright (c) 2021 Игорь Евгеньевич Войтецкий, Таисия Александровна Войтецкая, Леонид Викторович Вишневський, Игорь Петрович Козырев, Оксана Борисовна Максимова, Максим Витальевич Максимов, Виктория Игоревна Крывда

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.