A data-driven state identification method for intelligent control of the joint station export system
Abstract As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Opt...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87283-2 |
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| author | Guangli Xu Yifu Wang Zhihao Zhou Yifeng Lu Liangxue Cai |
| author_facet | Guangli Xu Yifu Wang Zhihao Zhou Yifeng Lu Liangxue Cai |
| author_sort | Guangli Xu |
| collection | DOAJ |
| description | Abstract As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. Based on the PSO-GWO-BP pressure drop prediction model, the determination method of state identification threshold is established, and a state identification method based on dynamic threshold is proposed, which realizes the intelligent identification of the system operation state by automatically adjusting the threshold. Through the analysis of the production and operation data of the joint station, the abnormal working conditions are successfully identified, and the effectiveness and accuracy of the method are verified. This method not only enhances the ability to discriminate abnormal working conditions but also adaptively adjusts the operation scheme, which effectively improves the intelligence level of the joint station export system. |
| format | Article |
| id | doaj-art-569178fe05e542eab64a3810f04bb52d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-569178fe05e542eab64a3810f04bb52d2025-01-26T12:24:14ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-87283-2A data-driven state identification method for intelligent control of the joint station export systemGuangli Xu0Yifu Wang1Zhihao Zhou2Yifeng Lu3Liangxue Cai4School of Oil & Natural Gas Engineering, Southwest Petroleum UniversitySchool of Oil & Natural Gas Engineering, Southwest Petroleum UniversitySchool of Oil & Natural Gas Engineering, Southwest Petroleum UniversitySchool of Oil & Natural Gas Engineering, Southwest Petroleum UniversitySchool of Oil & Natural Gas Engineering, Southwest Petroleum UniversityAbstract As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. Based on the PSO-GWO-BP pressure drop prediction model, the determination method of state identification threshold is established, and a state identification method based on dynamic threshold is proposed, which realizes the intelligent identification of the system operation state by automatically adjusting the threshold. Through the analysis of the production and operation data of the joint station, the abnormal working conditions are successfully identified, and the effectiveness and accuracy of the method are verified. This method not only enhances the ability to discriminate abnormal working conditions but also adaptively adjusts the operation scheme, which effectively improves the intelligence level of the joint station export system.https://doi.org/10.1038/s41598-025-87283-2State identificationParameter predictionGWOPSOBP neural network |
| spellingShingle | Guangli Xu Yifu Wang Zhihao Zhou Yifeng Lu Liangxue Cai A data-driven state identification method for intelligent control of the joint station export system Scientific Reports State identification Parameter prediction GWO PSO BP neural network |
| title | A data-driven state identification method for intelligent control of the joint station export system |
| title_full | A data-driven state identification method for intelligent control of the joint station export system |
| title_fullStr | A data-driven state identification method for intelligent control of the joint station export system |
| title_full_unstemmed | A data-driven state identification method for intelligent control of the joint station export system |
| title_short | A data-driven state identification method for intelligent control of the joint station export system |
| title_sort | data driven state identification method for intelligent control of the joint station export system |
| topic | State identification Parameter prediction GWO PSO BP neural network |
| url | https://doi.org/10.1038/s41598-025-87283-2 |
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