Information-extreme machine learning of the control system over the power unit of a thermal power main line
DOI:
https://doi.org/10.15587/1729-4061.2017.112121Keywords:
information-extreme intelligent technology, machine learning, decision making support system, information criterion, power unit, thermal power plantAbstract
The study considers a method of deep machine learning of a decision-making support system of control of a power unit of a thermal power plant. We developed a method within the framework of information-extreme intelligent technology, it is based on maximization of informational capacity of a control system in the process of machine learning. We developed categorical models of information-extreme machine learning with optimization of control tolerances to recognition attributes and levels of selection of coordinates of averaged binary vector-realizations of recognition classes. We considered a modified Kullback information criterion as a criterion for optimization of learning parameters. We implemented algorithms of machine learning with polymodal and unimodal decisive rules. We formed a learning matrix based on archival data of the operation of Shostka thermal and power plant. The results of physical modeling showed that the use of polymodal decisive rules does not provide a high functional efficiency of machine learning. We ordered the alphabet of recognition classes to the magnitude of deviation of a functional state of the technological process from the standard regime for the application of unimodal decisive rules. At the same time, we constructed unimodal decisive rules according to geometric parameters of hyper-spherical containers of recognition classes х by the enclosed structure. We proved experimentally that the use of the unimodal classifier gives possibility to construct decisive rules, which error-free by a learning matrix. The obtained results give possibility to provide high functional efficiency of machine learning of control systems of technological processes whose classes of recognition intersect substantially in a space of attributes.
References
- Eremin, E., Temchenko, D. (2009). Adaptive and robust control of objects of thermal power engineering. Blagoveshchensk: Amur state university, 228.
- Dorf, R. C., Bishop, R. H. (2011). Modern Control Systems. Prentice Hall, 1082.
- Han, J., Kamber, M., Pei, J. (2012). Data mining: concepts and techniques. Elsevier, 744.
- Subbotin, S., Oleinik, An., Hoffman, E., Zaitsev, S., Oleinik, Al.; Subbotin, S. (Ed.) (2012). Intelligent Information Technologies for the Designing of Automated Systems for Diagnosing and Recognizing Images. Kharkiv: SMIT Company, 318.
- Campbell, C., Ying, Y. (2011). Learning with Support Vector Machines. Morgan and Claypool, 95.
- Mikulandrić, R., Lončar, D., Cvetinović, D., Spiridon, G. (2013). Improvement of existing coal fired thermal power plants performance by control systems modifications. Energy, 57, 55–65. doi: 10.1016/j.energy.2013.02.033
- Kumar, S., Cherukuri, R. (2007). A Survey on Artificial Intelligence Techniques in Power Station. International Journal of Innovative Research in Science, Engineering and Technology, 6 (1), 852–861.
- Mayadevi, N., Vinodchandra, S. S., Ushakumari, S. (2014). A Review on Expert System Applications in Power Plants. International Journal of Electrical and Computer Engineering (IJECE), 4 (1), 116–126. doi: 10.11591/ijece.v4i1.5025
- Ongsakul, W., Dieu, V. N. (2013). Artificial Intelligence in Power System Optimization. CRC Press, 520.
- De Oliveira, M. V., De Almeida, J. C. S. (2013). Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system. Progress in Nuclear Energy, 63, 71–85. doi: 10.1016/j.pnucene.2012.11.005
- Sai, T. K., Reddy, K. A. (2015). Fuzzy Applications in a Power Station. International Journal on Soft Computing, 6 (2), 01–16. doi: 10.5121/ijsc.2015.6201
- Dovbysh, A. S. (2009). Foundation of intelligent systems designing. Sumy: Publisher SSU, 171.
- Dovbysh, A., Bibyk, M., Rudyi, A. (2015). Optimization of the parameters of teaching the control system of the power unit of the heat power plant. Compressor and Power Engineering, 3 (41), 37–40.
- Kutsenko, O., Kascheev, B., Mironenko, M. (2017). Geoinformation system of personnel identification at re-construction of the area. Bulletin of the NTU "KhPI". Series: System Analysis, Management and Information Technology, 28 (1250), 34–40.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Anatoliy Dovbysh, Dmytro Velykodnyi, Igor Shelehov, Myroslav Bibyk
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.
