{"title":"利用 MTGNN 和 Transformer 对具有物理约束的重型燃气轮机进行数据驱动建模的方法","authors":"","doi":"10.1016/j.conengprac.2024.106014","DOIUrl":null,"url":null,"abstract":"<div><p>Aiming at the complex nonlinear and uncertain multi-variable coupling problems of heavy-duty gas turbine, a data-driven heavy-duty gas turbine modeling method combined with physical constraint is proposed. Firstly, by combining the advantages of MTGNN that focuses on local features and Transformer that extracts global information, an improved network is constructed to model heavy-duty gas turbine with full operating conditions and wide load variation. Secondly, the physical constraint related to the mechanism of heavy-duty gas turbine is added to eliminate the unreasonable output in the proposed heavy-duty gas turbine model, which improves the reliability and interpretability of the proposed modeling approach. Then, the Lyapunov function is utilized to prove the convergence of the proposed modeling approach. Finally, the actual datasets of heavy-duty gas turbine are used to carry out relevant emulation and comparison experiments. The results show that the proposed modeling approach is superior to other typical network models, which verifies the accuracy and practicability of the proposed modeling approach.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven modeling approach of heavy-duty gas turbine with physical constraint by MTGNN and Transformer\",\"authors\":\"\",\"doi\":\"10.1016/j.conengprac.2024.106014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Aiming at the complex nonlinear and uncertain multi-variable coupling problems of heavy-duty gas turbine, a data-driven heavy-duty gas turbine modeling method combined with physical constraint is proposed. Firstly, by combining the advantages of MTGNN that focuses on local features and Transformer that extracts global information, an improved network is constructed to model heavy-duty gas turbine with full operating conditions and wide load variation. Secondly, the physical constraint related to the mechanism of heavy-duty gas turbine is added to eliminate the unreasonable output in the proposed heavy-duty gas turbine model, which improves the reliability and interpretability of the proposed modeling approach. Then, the Lyapunov function is utilized to prove the convergence of the proposed modeling approach. Finally, the actual datasets of heavy-duty gas turbine are used to carry out relevant emulation and comparison experiments. The results show that the proposed modeling approach is superior to other typical network models, which verifies the accuracy and practicability of the proposed modeling approach.</p></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066124001746\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124001746","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Data-driven modeling approach of heavy-duty gas turbine with physical constraint by MTGNN and Transformer
Aiming at the complex nonlinear and uncertain multi-variable coupling problems of heavy-duty gas turbine, a data-driven heavy-duty gas turbine modeling method combined with physical constraint is proposed. Firstly, by combining the advantages of MTGNN that focuses on local features and Transformer that extracts global information, an improved network is constructed to model heavy-duty gas turbine with full operating conditions and wide load variation. Secondly, the physical constraint related to the mechanism of heavy-duty gas turbine is added to eliminate the unreasonable output in the proposed heavy-duty gas turbine model, which improves the reliability and interpretability of the proposed modeling approach. Then, the Lyapunov function is utilized to prove the convergence of the proposed modeling approach. Finally, the actual datasets of heavy-duty gas turbine are used to carry out relevant emulation and comparison experiments. The results show that the proposed modeling approach is superior to other typical network models, which verifies the accuracy and practicability of the proposed modeling approach.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.