A new method for axis adjustment of the hydro-generator unit using machine learning
Abstract The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-30121-0 |
| _version_ | 1797865081241862144 |
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| author | Jie Cao Yang Li Zhaoyang Qu Yunchang Dong Yaowei Liu Ruxuan Zhang |
| author_facet | Jie Cao Yang Li Zhaoyang Qu Yunchang Dong Yaowei Liu Ruxuan Zhang |
| author_sort | Jie Cao |
| collection | DOAJ |
| description | Abstract The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new method for axis adjustment of hydro-generator unit based on an improved grey prediction model and swarms intelligence optimization neural network. First of all, it proposes a sequence acceleration translation and mean value transformation method, which is used to pre-process the axis net total swing sequence that exhibits oscillating fluctuations. It uses e 1 and e 2 factor transformation to establish an improved axis net total swing gray prediction model. Then, the advanced flamingo search algorithm is used to search the maximum value of the sine function of the net total pendulum of the axis, and the axis adjustment orientation is obtained. This method solves the problem that GM(1, 1) can only be predicted by monotone sequence in the past and the problem that the search algorithm is easy to fall into local optimum, effectively improves the calculation efficiency of axis and shorts the search time. Simulation examples show that the proposed method can significantly improve accuracy of axis adjustment. This method greatly improves the efficiency of azimuth search for axis adjustment. |
| first_indexed | 2024-04-09T23:02:18Z |
| format | Article |
| id | doaj.art-e9c798927d9f42cab447fb94f251653b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:02:18Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e9c798927d9f42cab447fb94f251653b2023-03-22T10:54:48ZengNature PortfolioScientific Reports2045-23222023-02-0113111610.1038/s41598-023-30121-0A new method for axis adjustment of the hydro-generator unit using machine learningJie Cao0Yang Li1Zhaoyang Qu2Yunchang Dong3Yaowei Liu4Ruxuan Zhang5School of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversitySchool of Electrical Engineering, Northeast Electric Power UniversityState Grid Jilin Electric Power Co., Ltd.School of Computer Science, Northeast Electric Power UniversityAbstract The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new method for axis adjustment of hydro-generator unit based on an improved grey prediction model and swarms intelligence optimization neural network. First of all, it proposes a sequence acceleration translation and mean value transformation method, which is used to pre-process the axis net total swing sequence that exhibits oscillating fluctuations. It uses e 1 and e 2 factor transformation to establish an improved axis net total swing gray prediction model. Then, the advanced flamingo search algorithm is used to search the maximum value of the sine function of the net total pendulum of the axis, and the axis adjustment orientation is obtained. This method solves the problem that GM(1, 1) can only be predicted by monotone sequence in the past and the problem that the search algorithm is easy to fall into local optimum, effectively improves the calculation efficiency of axis and shorts the search time. Simulation examples show that the proposed method can significantly improve accuracy of axis adjustment. This method greatly improves the efficiency of azimuth search for axis adjustment.https://doi.org/10.1038/s41598-023-30121-0 |
| spellingShingle | Jie Cao Yang Li Zhaoyang Qu Yunchang Dong Yaowei Liu Ruxuan Zhang A new method for axis adjustment of the hydro-generator unit using machine learning Scientific Reports |
| title | A new method for axis adjustment of the hydro-generator unit using machine learning |
| title_full | A new method for axis adjustment of the hydro-generator unit using machine learning |
| title_fullStr | A new method for axis adjustment of the hydro-generator unit using machine learning |
| title_full_unstemmed | A new method for axis adjustment of the hydro-generator unit using machine learning |
| title_short | A new method for axis adjustment of the hydro-generator unit using machine learning |
| title_sort | new method for axis adjustment of the hydro generator unit using machine learning |
| url | https://doi.org/10.1038/s41598-023-30121-0 |
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