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Neural network for predicting operating modes and calculating energy characteristics of steam turbine plants
https://doi.org/10.24223/1999-5555-2024-17-1-12-18
Abstract
The article describes the results of work on the creation of neural networks calculating the technical and economic parameters of all possible modes of operation of a thermal turbine unit with a steam turbine of type PT-60-130/13. In accordance with the objectives set, recommendations for the preparation of training data samples are formulated. The input and output parameters of the condensing and heating modes of operation of the steam turbine are determined. The results of research on determining the most optimal architecture of neural networks for calculating the energy characteristics of steam turbine plants of the heating type are presented. The results of calculations of the mean squared errors of neural network predictions from the results of calculations performed using a verified object-oriented model of a PT-60-130/13 turbine unit are tabulated.
Graphs of the dependence of the specific heat consumption for the generation of electrical energy on the power of a PT-60-130/13 turbine unit for condensation and heating modes of operation using neural networks are plotted. The conclusion is formulated about the possibility of using neural networks for the development of energy characteristics and regulatory documentation on fuel use of equipment of thermal power plants.
About the Author
A. Y. Gubarev
Samara State Technical University
Russian Federation
Russian Federation
443100 Molodogvardeyskaya str., 244, Samara
References
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Review
For citations:
Gubarev A.Y. Neural network for predicting operating modes and calculating energy characteristics of steam turbine plants. Safety and Reliability of Power Industry. 2024;17(1):12-18. (In Russ.) https://doi.org/10.24223/1999-5555-2024-17-1-12-18
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