Skip to main content
SpringerLink
Log in

Set of Neural Network Models for Intelligent Control of Low- and Medium-Capacity Gas-Turbine Power Plants

  • Published:
Russian Electrical Engineering Aims and scope Submit manuscript

Abstract

The idea of creating a set of neural network models consists in that, due to the widespread use of distributed power generation and an increasingly frequent use of low- and medium-capacity gas-turbine power plants, it is required to ensure a stable and reliable production of electricity at required quality levels. The solution proposed to solve this task is to develop intelligent control systems and new control algorithms for gas-turbine power plants, considering the behavior of the whole structure of the electrical system on the basis of the model-oriented approach with the help of a set of neural network models. This approach opens a broad range of possibilities for studying and taking into account, first of all, the whole diversity of performance situations and design layouts of the power system and, second, the whole diversity of advanced techniques of the theory of automatic control for running individual energy modules in performance situations emerging in the energy system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.

Similar content being viewed by others

REFERENCES

  1. Gol’berg, F.D. and Batenini, A.V., Matematicheskie modeli gazoturbinnykh dvigatelei kak ob”ektov upravleniya (Mathematical Models of GTE as the Controlled Objects), Moscow: Mosk. Aviats. Inst., 1999.

  2. Asgari, H., Chen, X.Q., Menhaj, M.B., and Sainudiin, R., Artificial neural network-based system identification for a single-shaft gas turbine, J. Eng. Gas Turbines Power, 2013, vol. 135, no. 9.

  3. Asgari, H., Chen, X.Q., and Sainudiin, R., Modeling and simulation of gas turbines, Int. J. Model., Identif. Control, 2013, vol. 20, no. 3.

  4. Asgari, H. and Chen, X.Q., Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks, Boca Raton, FL: CRC Press, 2015.

    Book  Google Scholar 

  5. Kilin, G.A., Adjesment of neural network parameters for a mathematical model of a gas turbine power plant, Materialy Mezhdunarodnoi nauchno-prakticheskoi konferentsii “Nauka segodnya: Zadachi i puti ikh resheniya” (Proc. Int. Sci.-Pract. Conf. Modern Science: Problems and Their Solutions”), Vologda: Marker, 2016.

  6. Kilin, G.A., Kavalerov, B.V., and Masyagin, E.D., The choice of neural network architecture to design a mathematical model of a gas turbine power plant, Materialy Mezhdunarodnoi nauchno-prakticheskoi konferentsii “Aktual’nye problemy elektromekhaniki i elektrotekhnologii” (Proc. Sci.-Pract. Conf. “Current Problems in Electromechanics and Electrical Technologies”), Yekaterinburg: Ural. Fed. Univ., 2017.

  7. Kilin, G.A., Bakhirev, I.V., and Kavalerov, B.V., Nonlinear GTU model receiving by neural network, Avtom. Elektroenerg. Elektrotekh., 2015, vol. 1.

    Google Scholar 

  8. Kavalerov, B.V. and Kilin, G.A., Electric gas-turbine unit control system automation tuning based on neural network models, Inf.-Izmerit. Upr. Sist., 2018, no. 9.

  9. Kilin, G.A. and Kavalerov, B.V., Development of a mathematical model of a gas turbine power plant based on neural networks, Materialy nauchno-tekhnicheskoi konferentsii “Klimovskie chteniya–2016: Perspektivnye napravleniya razvitiya aviadvigatelestroeniya” (Proc. Sci.-Tech. Conf. “Klimov’s Readings–2017: Advanced Development of Aircraft Engine Manufacturing”), St. Petersburg: Skifiya-Print, 2016.

  10. Haykin, S.S., Neural Networks: A Comprehensive Foundation, New York: Macmillan, 1994.

    MATH  Google Scholar 

  11. Callan, R., The Essence of Neural Networks, London: Prentice Hall, 1999.

    Google Scholar 

Download references

Funding

This work was financially supported by the Russian Foundation for Basic Research and the government of Perm krai, research project 19-48-590012.

Author information

Authors and Affiliations

  1. Perm National Research Polytechnic University, 614990, Perm, Russia

    G. A. Kilin, B. V. Kavalerov & A. I. Suslov

Authors
  1. G. A. Kilin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. V. Kavalerov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. I. Suslov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. A. Kilin.

Additional information

Translated by S. Kuznetsov

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kilin, G.A., Kavalerov, B.V. & Suslov, A.I. Set of Neural Network Models for Intelligent Control of Low- and Medium-Capacity Gas-Turbine Power Plants. Russ. Electr. Engin. 91, 659–664 (2020). https://doi.org/10.3103/S1068371220110085

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068371220110085

Keywords:

Search

Navigation