Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.
The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0275998 |
| _version_ | 1797989984241713152 |
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| author | Yanning Lu Yanzheng Xiang Bo Chen Haiyang Zhu Junfeng Yue Yawei Jin Pengfei He Yibo Zhao Yingjie Zhu Jiasheng Si Deyu Zhou |
| author_facet | Yanning Lu Yanzheng Xiang Bo Chen Haiyang Zhu Junfeng Yue Yawei Jin Pengfei He Yibo Zhao Yingjie Zhu Jiasheng Si Deyu Zhou |
| author_sort | Yanning Lu |
| collection | DOAJ |
| description | The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam turbine individually while ignoring the coupling relationship with the condenser, which we believe is crucial for the prediction. Therefore, in this paper, to explore the coupling relationship between steam turbine and condenser, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). In specific, the historical information within condenser operation conditions data is encoded using a long-short term memory network. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network is incorporated to capture the local and global information in the encoder. The steam turbine power is predicted based on all the information. In this way, the coupling relationship between the condenser and the steam turbine is fully explored. Abundant experiments are conducted on real data from the power plant. The experimental results show that our proposed CVD-EDF achieves great improvements over several competitive methods. our method improves by 32.2% and 37.0% in terms of RMSE and MAE by comparing the LSTM at one-minute intervals. |
| first_indexed | 2024-04-11T08:29:21Z |
| format | Article |
| id | doaj.art-bb4ff0a04d024bc0871683af9d91f75f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T08:29:21Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-bb4ff0a04d024bc0871683af9d91f75f2022-12-22T04:34:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710e027599810.1371/journal.pone.0275998Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.Yanning LuYanzheng XiangBo ChenHaiyang ZhuJunfeng YueYawei JinPengfei HeYibo ZhaoYingjie ZhuJiasheng SiDeyu ZhouThe steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam turbine individually while ignoring the coupling relationship with the condenser, which we believe is crucial for the prediction. Therefore, in this paper, to explore the coupling relationship between steam turbine and condenser, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). In specific, the historical information within condenser operation conditions data is encoded using a long-short term memory network. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network is incorporated to capture the local and global information in the encoder. The steam turbine power is predicted based on all the information. In this way, the coupling relationship between the condenser and the steam turbine is fully explored. Abundant experiments are conducted on real data from the power plant. The experimental results show that our proposed CVD-EDF achieves great improvements over several competitive methods. our method improves by 32.2% and 37.0% in terms of RMSE and MAE by comparing the LSTM at one-minute intervals.https://doi.org/10.1371/journal.pone.0275998 |
| spellingShingle | Yanning Lu Yanzheng Xiang Bo Chen Haiyang Zhu Junfeng Yue Yawei Jin Pengfei He Yibo Zhao Yingjie Zhu Jiasheng Si Deyu Zhou Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. PLoS ONE |
| title | Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. |
| title_full | Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. |
| title_fullStr | Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. |
| title_full_unstemmed | Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. |
| title_short | Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. |
| title_sort | steam turbine power prediction based on encode decoder framework guided by the condenser vacuum degree |
| url | https://doi.org/10.1371/journal.pone.0275998 |
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