Data Mining for State Evaluation and Analysis of Thermal Power Generators

DOI

10.2991/978-94-6463-056-5_94How to use a DOI?

Keywords

data mining technology; thermal power generator; state assessment

ABSTRACT

At this stage, with the progress of industrial science, my country's thermal power units are constantly supplementing the shortcomings, the total installed capacity has increased, and some power plants that have been eliminated by the times and are not conducive to environmentally friendly development have been shut down. The thermal power units currently in use are not only safe and reliable, but also attach great importance to intelligent power generation and refined tubes, especially the maintenance and management of thermal power generators. At present, a new data mining technology can be applied to the condition maintenance of thermal power units. This technology obtains potential information from data to help power plant personnel identify the operating status of the unit, so as to achieve better operation and maintenance of thermal power generating units. Based on this It can improve the economy and reliability of thermal power generators and promote the improvement of the intelligent level of power plants.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuanzong Zhao
AU  - Guangyu Yang
AU  - Wenlin Yang
AU  - Xiaozhong Chen
AU  - Sen Wang
PY  - 2022
DA  - 2022/12/29
TI  - Data Mining for State Evaluation and Analysis of Thermal Power Generators
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 646
EP  - 650
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_94
DO  - 10.2991/978-94-6463-056-5_94
ID  - Zhao2022
ER  -