Applied Artificial Intelligence An International Journal
ISSN: 0883-9514 (Print) 1087-6545 (Online) Journal homepage: http://www.tandfonline.com/loi/uaai20
Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data Abdelhafid Benyounes, Ahmed Hafaifa & Mouloud Guemana To cite this article: Abdelhafid Benyounes, Ahmed Hafaifa & Mouloud Guemana (2016) Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data, Applied Artificial Intelligence, 30:1, 29-51 To link to this article: http://dx.doi.org/10.1080/08839514.2016.1138808
Published online: 18 Feb 2016.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=uaai20 Download by: [41.107.89.153]
Date: 19 February 2016, At: 00:26
APPLIED ARTIFICIAL INTELLIGENCE 2016, VOL. 30, NO. 1, 29–51 http://dx.doi.org/10.1080/08839514.2016.1138808
Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data Abdelhafid Benyounesa, Ahmed Hafaifaa, and Mouloud Guemanab a Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa, Algeria; bFaculty of Science and Technology, University of Médéa, Médéa, Algeria
Downloaded by [41.107.89.153] at 00:26 19 February 2016
ABSTRACT
The development of reliable mathematical models for nonlinear systems has been a primary topic in several industrial applications. This work proposes to examine the application of fuzzy logic to represent the control parameters of a gas turbine based on the fuzzy clustering method using Gustafson–Kessel algorithms. The results obtained from data classification of construction with associated models indicate applications in modeling the examined system.
Introduction To reflect the overall characteristics of industrial systems and their operating conditions, prior models to supervise and control a system need to be established to maximize the performance in practice. The implementation of the governing equations in industrial systems usually leads to an excessively complex model of knowledge with control approaches that are delicate to implement (Feil, Abonyi, and Szeifert 2004; Kim, Ko, and Perez-Blanco 2011; Takagi and Sugeno 1985). In this case, the use of modeling techniques based on measured input/output collected for the system is required in order to reduce the complexity of modeling complex systems to improve the system performance (Dunn 1973; Gustafson and Kessel 1978; Bezdek and Douglas Harris 1979). This work proposes the use of fuzzy techniques for modeling the parameters of a gas turbine. The examined gas turbine is used to drive multistorey centrifugal compressors, and the data have been collected in real time in order to exploit the studied gas turbine. The quality of the universal fuzzy approximators, which has been approved in several works, transforms these nonlinear systems into a set of linear systems (Abonyi, Babuska, and Szeifert 2002; Ahmed et al. 2012; Robert 1998; Buckley 1992; Setnes, Babuška, and Verbruggen 1998; Vernieuwe, De Baets, and Verhoest 2004). Expert knowledge was not available in our case; the structure must be identified from the data. A fuzzy classification method can CONTACT Ahmed Hafaifa
[email protected] Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, City ben Aziez, Bloc 73 n°20 W. Djelfa, Algeria. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/UAAI. © 2016 Taylor & Francis
Downloaded by [41.107.89.153] at 00:26 19 February 2016
30
A. BENYOUNES ET AL.
then be used to partition the data space into several classes. The use of a fuzzy clustering algorithm has an important advantage; it allows the automatic generation of fuzzy membership or data from regions’ functions. These models are constructed by minimizing a cost function. Fuzzy identification methods have been developed from experimental data modeling in several works; the supervised and unsupervised learning methods are among the most-used fuzzy classification techniques (Babuška and Verbruggen 1995; Ruspini 1969; Barkó, Abonyi, and Hlavay 1999; Bezdek, Ehrlich, and Full 1984; Topalov et al. 2007; Abonyi et al. 2005). The works developed by Takagi and Sugeno (1985) show that fuzzy techniques are effective tools for approximating nonlinear systems. Other techniques developed by Babuška et al. in (Abonyi, Babuška, and Szeifert 2002; Babuška and Verbruggen 1995; Robert 1998) have been used to construct these combined techniques as fuzzy least squares models. This work attempted to examine the application of fuzzy logic to represent the dice control parameters of a gas turbine based on the fuzzy clustering method using Gustafson–Kessel algorithms. The integration of these algorithms to provide real-time regulation will result in the recovery of the loss of gas production by ensuring optimal gas turbine operation. Several studies have focused on the problem of modeling this turbine and presented rigorous models with a complex mathematical structure, which significantly limited the possibility of their direct use via conventional control methods (Ablay 2013; Ahmed, Guemana, and Daoudi 2015; Jurado and Carpio 2006; Nikpey et al. 2014; Topalov 2011). These models were established at nominal operating conditions that hinder the control of the temperature and flow suction in this compression system. To this end, this work proposes a fuzzy model that describes the dynamics of the gas turbine system. Using various tests, this study confirms that the obtained results clearly demonstrate the recurred main dynamic characteristics of the gas turbine system when using the proposed fuzzy model, enabling better performance during its operation for control synthesis. Modeling based on fuzzy clustering Today, specialized automatic control systems have become increasingly complex by considering not only the control problems but also low-supervisory-control problem layers, surveillance problems, diagnosis, and driving assistance. Therefore, the complexity of industrial systems is a dimension to which the different levels of modeling are added, which must have reliable mathematical models for implementation. Consequently, the nature of the information for handling industrial systems is significantly different compared with that for conventional control problems. Information for industrial systems is often derived from multiple sources, is qualitative in nature, and the data are usually imprecise and uncertain.
APPLIED ARTIFICIAL INTELLIGENCE
31
This article proposes the development of qualitative formalisms that elucidate various aspects of the methods and data processing, such as the methods based on fuzzy clustering. For these methods of clustering, the data are typically observations (measurements) from a certain physical process. Each kth observation is a vector with "zk 2
